Publications

2018
[1]
S. Degaetano-Ortlieb and J. Strötgen, “Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy,” in Language Technologies for the Challenges of the Digital Age (GSCL 2017), Berlin, Germany, 2018.
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@inproceedings{DegaetanoortliebStroetgen2017, TITLE = {Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy}, AUTHOR = {Degaetano-Ortlieb, Stefania and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-3-319-73705-8}, DOI = {10.1007/978-3-319-73706-5_22}, PUBLISHER = {Springer}, YEAR = {2017}, DATE = {2018}, BOOKTITLE = {Language Technologies for the Challenges of the Digital Age (GSCL 2017)}, EDITOR = {Rehm, Georg and Declerck, Thierry}, PAGES = {259--275}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {10713}, ADDRESS = {Berlin, Germany}, }
Endnote
%0 Conference Proceedings %A Degaetano-Ortlieb, Stefania %A Strötgen, Jannik %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Diachronic Variation of Temporal Expressions in Scientific Writing through the Lens of Relative Entropy : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-A8E8-5 %R 10.1007/978-3-319-73706-5_22 %D 2018 %B Conference of the German Society for Computational Linguistics and Language Technology %Z date of event: 2017-09-13 - 2017-09-14 %C Berlin, Germany %B Language Technologies for the Challenges of the Digital Age %E Rehm, Georg; Declerck, Thierry %P 259 - 275 %I Springer %@ 978-3-319-73705-8 %B Lecture Notes in Artificial Intelligence %N 10713
[2]
J. Kalofolias, E. Galbrun, and P. Miettinen, “From Sets of Good Redescriptions to Good Sets of Redescriptions,” Knowledge and Information Systems, 2018.
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@article{kalofolias18from, TITLE = {From Sets of Good Redescriptions to Good Sets of Redescriptions}, AUTHOR = {Kalofolias, Janis and Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISSN = {0219-1377}, DOI = {10.1007/s10115-017-1149-7}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2018}, JOURNAL = {Knowledge and Information Systems}, }
Endnote
%0 Journal Article %A Kalofolias, Janis %A Galbrun, Esther %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T From Sets of Good Redescriptions to Good Sets of Redescriptions : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-90D1-5 %R 10.1007/s10115-017-1149-7 %7 2018-01-19 %D 2018 %8 19.01.2018 %J Knowledge and Information Systems %I Springer %C New York, NY %@ false
[3]
S. Karaev, J. Hook, and P. Miettinen, “Latitude: A Model for Mixed Linear-Tropical Matrix Factorization,” 2018. [Online]. Available: http://arxiv.org/abs/1801.06136. (arXiv: 1801.06136)
Abstract
Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.
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@online{Karaev2018, TITLE = {Latitude: A Model for Mixed Linear-Tropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Hook, James and Miettinen, Pauli}, URL = {http://arxiv.org/abs/1801.06136}, EPRINT = {1801.06136}, EPRINTTYPE = {arXiv}, YEAR = {2018}, ABSTRACT = {Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.}, }
Endnote
%0 Report %A Karaev, Sanjar %A Hook, James %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Latitude: A Model for Mixed Linear-Tropical Matrix Factorization : %U http://hdl.handle.net/21.11116/0000-0000-636B-9 %U http://arxiv.org/abs/1801.06136 %D 2018 %X Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis. A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components. Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation. In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two. In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures. We present an algorithm for our novel matrix factorization. Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone. %K Computer Science, Learning, cs.LG
[4]
J. Strötgen, A.-L. Minard, L. Lange, M. Speranza, and B. Magnini, “KRAUTS: A German Temporally Annotated News Corpus,” in Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. (Accepted/in press)
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@inproceedings{StroetgenELREC2018, TITLE = {{KRAUTS}: {A German} Temporally Annotated News Corpus}, AUTHOR = {Str{\"o}tgen, Jannik and Minard, Anne-Lyse and Lange, Lukas and Speranza, Manuela and Magnini, Bernardo}, LANGUAGE = {eng}, URL = {http://lrec2018.lrec-conf.org/en/}, YEAR = {2018}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, ADDRESS = {Miyazaki, Japan}, }
Endnote
%0 Conference Proceedings %A Strötgen, Jannik %A Minard, Anne-Lyse %A Lange, Lukas %A Speranza, Manuela %A Magnini, Bernardo %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T KRAUTS: A German Temporally Annotated News Corpus : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-8B8C-E %U http://lrec2018.lrec-conf.org/en/ %D 2017 %B 11th Language Resources and Evaluation Conference %Z date of event: 2018-05-07 - 2018-05-12 %C Miyazaki, Japan %B Eleventh International Conference on Language Resources and Evaluation
2017
[5]
A. Abujabal, R. S. Roy, M. Yahya, and G. Weikum, “QUINT: Interpretable Question Answering over Knowledge Bases,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, 2017.
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@inproceedings{AbujabalENMLP2017, TITLE = {{QUINT}: {I}nterpretable Question Answering over Knowledge Bases}, AUTHOR = {Abujabal, Abdalghani and Roy, Rishiraj Saha and Yahya, Mohamed and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-945626-97-5}, URL = {http://aclweb.org/anthology/D17-2011}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, PAGES = {61--66}, ADDRESS = {Copenhagen, Denmark}, }
Endnote
%0 Conference Proceedings %A Abujabal, Abdalghani %A Roy, Rishiraj Saha %A Yahya, Mohamed %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T QUINT: Interpretable Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-F97C-E %U http://aclweb.org/anthology/D17-2011 %D 2017 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2017-09-09 - 2017-09-11 %C Copenhagen, Denmark %B The Conference on Empirical Methods in Natural Language Processing %P 61 - 66 %I ACL %@ 978-1-945626-97-5 %U http://aclweb.org/anthology/D17-2011
[6]
A. Abujabal, M. Yahya, M. Riedewald, and G. Weikum, “Automated Template Generation for Question Answering over Knowledge Graphs,” in WWW’17, 26th International Conference on World Wide Web, Perth, Australia, 2017.
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@inproceedings{AbujabalWWW2017, TITLE = {Automated Template Generation for Question Answering over Knowledge Graphs}, AUTHOR = {Abujabal, Abdalghani and Yahya, Mohamed and Riedewald, Mirek and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4913-0}, DOI = {10.1145/3038912.3052583}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17, 26th International Conference on World Wide Web}, PAGES = {1191--1200}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Abujabal, Abdalghani %A Yahya, Mohamed %A Riedewald, Mirek %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Automated Template Generation for Question Answering over Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4F9C-E %R 10.1145/3038912.3052583 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 %P 1191 - 1200 %I ACM %@ 978-1-4503-4913-0
[7]
P. Agarwal, “Time-Aware Named Entity Disambiguation,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
Named Entity Disambiguation (NED) is a Natural Language Processing task of linking mentions of named entities is a text to their corresponding entries in a Knowledge Base. It serves as a crucial component in applications such as Semantic Search, Knowledge Base Population, and Opinion Mining. Currently deployed tools for NED are based on sophisticated models that use coherence relation among enti- ties and distributed vectors to represent the entity mentions and their contexts in a document to disambiguate them collectively. Factors that have not been considered yet in this track are the semantics of temporal information about canonical entity forms and their mentions. Even though temporal expressions in a text give inherent structural characteristic to it, for instance, it can map a topic being discussed to a certain period of known history, yet such expressions are leveraged no differently than other dictionary words. In this thesis we propose the first time-aware NED model, which extends a state-of-the-art learning to rank approach based on joint word-entity embeddings. For this we introduce the concept of temporal signatures that is used in our work to represent the importance of each entity in a Knowledge Base over a historical time-line. Such signatures for the entities and temporal con- texts for the entity mentions are represented in our proposed temporal vector space to model the similarities between them. We evaluated our method on CoNLL-AIDA and TAC 2010, which are two widely used datasets in the NED track. However, be- cause such datasets are composed of news articles from a short time-period, they do not provide extensive evaluation for our proposed temoral similarity modeling. Therefore, we curated a dia-chronic dataset, diaNED, with the characteristic of temporally diverse entity mentions in its text collection.
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@mastersthesis{Agarwalmaster2017, TITLE = {Time-Aware Named Entity Disambiguation}, AUTHOR = {Agarwal, Prabal}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Named Entity Disambiguation (NED) is a Natural Language Processing task of linking mentions of named entities is a text to their corresponding entries in a Knowledge Base. It serves as a crucial component in applications such as Semantic Search, Knowledge Base Population, and Opinion Mining. Currently deployed tools for NED are based on sophisticated models that use coherence relation among enti- ties and distributed vectors to represent the entity mentions and their contexts in a document to disambiguate them collectively. Factors that have not been considered yet in this track are the semantics of temporal information about canonical entity forms and their mentions. Even though temporal expressions in a text give inherent structural characteristic to it, for instance, it can map a topic being discussed to a certain period of known history, yet such expressions are leveraged no differently than other dictionary words. In this thesis we propose the first time-aware NED model, which extends a state-of-the-art learning to rank approach based on joint word-entity embeddings. For this we introduce the concept of temporal signatures that is used in our work to represent the importance of each entity in a Knowledge Base over a historical time-line. Such signatures for the entities and temporal con- texts for the entity mentions are represented in our proposed temporal vector space to model the similarities between them. We evaluated our method on CoNLL-AIDA and TAC 2010, which are two widely used datasets in the NED track. However, be- cause such datasets are composed of news articles from a short time-period, they do not provide extensive evaluation for our proposed temoral similarity modeling. Therefore, we curated a dia-chronic dataset, diaNED, with the characteristic of temporally diverse entity mentions in its text collection.}, }
Endnote
%0 Thesis %A Agarwal, Prabal %Y Strötgen, Jannik %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Time-Aware Named Entity Disambiguation : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-9119-C %I Universität des Saarlandes %C Saarbrücken %D 2017 %P 114 p. %V master %9 master %X Named Entity Disambiguation (NED) is a Natural Language Processing task of linking mentions of named entities is a text to their corresponding entries in a Knowledge Base. It serves as a crucial component in applications such as Semantic Search, Knowledge Base Population, and Opinion Mining. Currently deployed tools for NED are based on sophisticated models that use coherence relation among enti- ties and distributed vectors to represent the entity mentions and their contexts in a document to disambiguate them collectively. Factors that have not been considered yet in this track are the semantics of temporal information about canonical entity forms and their mentions. Even though temporal expressions in a text give inherent structural characteristic to it, for instance, it can map a topic being discussed to a certain period of known history, yet such expressions are leveraged no differently than other dictionary words. In this thesis we propose the first time-aware NED model, which extends a state-of-the-art learning to rank approach based on joint word-entity embeddings. For this we introduce the concept of temporal signatures that is used in our work to represent the importance of each entity in a Knowledge Base over a historical time-line. Such signatures for the entities and temporal con- texts for the entity mentions are represented in our proposed temporal vector space to model the similarities between them. We evaluated our method on CoNLL-AIDA and TAC 2010, which are two widely used datasets in the NED track. However, be- cause such datasets are composed of news articles from a short time-period, they do not provide extensive evaluation for our proposed temoral similarity modeling. Therefore, we curated a dia-chronic dataset, diaNED, with the characteristic of temporally diverse entity mentions in its text collection.
[8]
P. Agarwal and J. Strötgen, “Tiwiki: Searching Wikipedia with Temporal Constraints,” in WWW ’17 Companion, Perth, Australia, 2017.
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@inproceedings{AgarwalStroetgen2017_TempWeb, TITLE = {Tiwiki: Searching {W}ikipedia with Temporal Constraints}, AUTHOR = {Agarwal, Prabal and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3051112}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW '17 Companion}, PAGES = {1595--1600}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Agarwal, Prabal %A Strötgen, Jannik %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Tiwiki: Searching Wikipedia with Temporal Constraints : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-53AE-9 %R 10.1145/3041021.3051112 %D 2017 %B 26th International Conference on World Wide Web Companion %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW '17 Companion %P 1595 - 1600 %I ACM %@ 978-1-4503-4914-7
[9]
R. Andrade and J. Strötgen, “All Dates Lead to Rome: Extracting and Explaining Temporal References in Street Names,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{AndradeWWW2017, TITLE = {All Dates Lead to {R}ome: {E}xtracting and Explaining Temporal References in Street Names}, AUTHOR = {Andrade, Rosita and Str{\"o}tgen, Jannik}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3054249}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {757--758}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Andrade, Rosita %A Strötgen, Jannik %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T All Dates Lead to Rome: Extracting and Explaining Temporal References in Street Names : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-62AE-1 %R 10.1145/3041021.3054249 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 757 - 758 %I ACM %@ 978-1-4503-4914-7
[10]
A. Bhattacharyya and J. Vreeken, “Efficiently Summarising Event Sequences with Rich Interleaving Patterns,” 2017. [Online]. Available: http://arxiv.org/abs/1701.08096. (arXiv: 1701.08096)
Abstract
Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose \ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show \ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values.
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@online{DBLP:journals/corr/BhattacharyyaV17, TITLE = {Efficiently Summarising Event Sequences with Rich Interleaving Patterns}, AUTHOR = {Bhattacharyya, Apratim and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1701.08096}, EPRINT = {1701.08096}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose \ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show \ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values.}, }
Endnote
%0 Report %A Bhattacharyya, Apratim %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiently Summarising Event Sequences with Rich Interleaving Patterns : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90E4-A %U http://arxiv.org/abs/1701.08096 %D 2017 %X Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose \ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show \ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB
[11]
A. Bhattacharyya and J. Vreeken, “Efficiently Summarising Event Sequences with Rich Interleaving Patterns,” in Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017), Houston, TX, USA, 2017.
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@inproceedings{bhattacharyya:17:squish, TITLE = {Efficiently Summarising Event Sequences with Rich Interleaving Patterns}, AUTHOR = {Bhattacharyya, Apratim and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-497-3}, DOI = {10.1137/1.9781611974973.89}, PUBLISHER = {SIAM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017)}, PAGES = {795--803}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Bhattacharyya, Apratim %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiently Summarising Event Sequences with Rich Interleaving Patterns : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4BDC-D %R 10.1137/1.9781611974973.89 %D 2017 %B 17th SIAM International Conference on Data Mining %Z date of event: 2017-04-27 - 2017-04-29 %C Houston, TX, USA %B Proceedings of the Seventeenth SIAM International Conference on Data Mining %P 795 - 803 %I SIAM %@ 978-1-61197-497-3
[12]
A. J. Biega, R. S. Roy, and G. Weikum, “Privacy through Solidarity: A User-Utility-Preserving Framework to Counter Profiling,” in SIGIR’17, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, 2017.
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@inproceedings{BiegaSIGIR2017, TITLE = {Privacy through Solidarity: {A} User-Utility-Preserving Framework to Counter Profiling}, AUTHOR = {Biega, Asia J. and Roy, Rishiraj Saha and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5022-8}, DOI = {10.1145/3077136.3080830}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {SIGIR'17, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {675--684}, ADDRESS = {Shinjuku, Tokyo, Japan}, }
Endnote
%0 Conference Proceedings %A Biega, Asia J. %A Roy, Rishiraj Saha %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Privacy through Solidarity: A User-Utility-Preserving Framework to Counter Profiling : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-F901-2 %R 10.1145/3077136.3080830 %D 2017 %B 40th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2017-08-07 - 2017-08-11 %C Shinjuku, Tokyo, Japan %B SIGIR'17 %P 675 - 684 %I ACM %@ 978-1-4503-5022-8
[13]
A. J. Biega, A. Ghazimatin, H. Ferhatosmanoglu, K. P. Gummadi, and G. Weikum, “Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities,” in CIKM’17, 26th ACM International Conference on Information and Knowledge Management, Singapore, Singapore, 2017.
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@inproceedings{Biega_CIKM2017, TITLE = {Learning to Un-Rank: {Q}uantifying Search Exposure for Users in Online Communities}, AUTHOR = {Biega, Asia J. and Ghazimatin, Azin and Ferhatosmanoglu, Hakan and Gummadi, Krishna P. and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4918-5}, DOI = {10.1145/3132847.3133040}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {CIKM'17, 26th ACM International Conference on Information and Knowledge Management}, PAGES = {267--276}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Biega, Asia J. %A Ghazimatin, Azin %A Ferhatosmanoglu, Hakan %A Gummadi, Krishna P. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3BA4-5 %R 10.1145/3132847.3133040 %D 2017 %B 26th ACM International Conference on Information and Knowledge Management %Z date of event: 2017-11-06 - 2017-11-10 %C Singapore, Singapore %B CIKM'17 %P 267 - 276 %I ACM %@ 978-1-4503-4918-5
[14]
N. Boldyrev, “Alignment of Multi-Cultural Knowledge Repositories,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration.
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@phdthesis{BOLDYREVPHD2017, TITLE = {Alignment of Multi-Cultural Knowledge Repositories}, AUTHOR = {Boldyrev, Natalia}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-269407}, DOI = {10.22028/D291-26940}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration.}, }
Endnote
%0 Thesis %A Boldyrev, Natalia %Y Weikum, Gerhard %A referee: Berberich, Klaus %A referee: Spaniol, Marc %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Alignment of Multi-Cultural Knowledge Repositories : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-87D8-2 %R 10.22028/D291-26940 %U urn:nbn:de:bsz:291-scidok-ds-269407 %I Universität des Saarlandes %C Saarbrücken %D 2017 %8 06.12.2017 %P X, 124 p. %V phd %9 phd %X The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26891
[15]
N. Boldyrev, M. Spaniol, J. Strötgen, and G. Weikum, “SESAME: European Statistics Explored via Semantic Alignment onto Wikipedia,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{BoldyrevWWW2017, TITLE = {{SESAME}: {E}uropean Statistics Explored via Semantic Alignment onto {Wikipedia}}, AUTHOR = {Boldyrev, Natalia and Spaniol, Marc and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3054732}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {177--181}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Boldyrev, Natalia %A Spaniol, Marc %A Strötgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T SESAME: European Statistics Explored via Semantic Alignment onto Wikipedia : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-80B0-0 %R 10.1145/3041021.3054732 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 177 - 181 %I ACM %@ 978-1-4503-4914-7
[16]
M. Boley, B. R. Goldsmith, L. M. Ghiringhelli, and J. Vreeken, “Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery,” Data Mining and Knowledge Discovery, vol. 31, no. 5, 2017.
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@article{Boley2017, TITLE = {Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery}, AUTHOR = {Boley, Mario and Goldsmith, Bryan R. and Ghiringhelli, Luca M. and Vreeken, Jilles}, LANGUAGE = {eng}, DOI = {10.1007/s10618-017-0520-3}, PUBLISHER = {Springer}, ADDRESS = {London}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Data Mining and Knowledge Discovery}, VOLUME = {31}, NUMBER = {5}, PAGES = {1391--1418}, }
Endnote
%0 Journal Article %A Boley, Mario %A Goldsmith, Bryan R. %A Ghiringhelli, Luca M. %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90E1-0 %R 10.1007/s10618-017-0520-3 %7 2017-06-28 %D 2017 %8 28.06.2017 %J Data Mining and Knowledge Discovery %V 31 %N 5 %& 1391 %P 1391 - 1418 %I Springer %C London
[17]
M. Boley, B. R. Goldsmith, L. M. Ghiringhelli, and J. Vreeken, “Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery,” 2017. [Online]. Available: http://arxiv.org/abs/1701.07696. (arXiv: 1701.07696)
Abstract
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.
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@online{DBLP:journals/corr/BoleyGGV17, TITLE = {Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery}, AUTHOR = {Boley, Mario and Goldsmith, Bryan R. and Ghiringhelli, Luca M. and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1701.07696}, EPRINT = {1701.07696}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.}, }
Endnote
%0 Report %A Boley, Mario %A Goldsmith, Bryan R. %A Ghiringhelli, Luca M. %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90DB-F %U http://arxiv.org/abs/1701.07696 %D 2017 %X Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Databases, cs.DB
[18]
K. Budhathoki and J. Vreeken, “Correlation by Compression,” in Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017), Houston, TX, USA, 2017.
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@inproceedings{budhathoki:17:cbc, TITLE = {Correlation by Compression}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-611974-87-4}, DOI = {10.1137/1.9781611974973.59}, PUBLISHER = {SIAM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017)}, EDITOR = {Chawla, Nitesh}, PAGES = {525--533}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Correlation by Compression : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4BD8-6 %R 10.1137/1.9781611974973.59 %D 2017 %B 17th SIAM International Conference on Data Mining %Z date of event: 2017-04-27 - 2017-04-29 %C Houston, TX, USA %B Proceedings of the Seventeenth SIAM International Conference on Data Mining %E Chawla, Nitesh; Wang, Wei %P 525 - 533 %I SIAM %@ 978-1-611974-87-4
[19]
K. Budhathoki and J. Vreeken, “Causal Inference by Stochastic Complexity,” 2017. [Online]. Available: http://arxiv.org/abs/1702.06776. (arXiv: 1702.06776)
Abstract
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes.
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@online{DBLP:journals/corr/BudhathokiV17, TITLE = {Causal Inference by Stochastic Complexity}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1702.06776}, EPRINT = {1702.06776}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes.}, }
Endnote
%0 Report %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference by Stochastic Complexity : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90F2-A %U http://arxiv.org/abs/1702.06776 %D 2017 %X The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion is not computable. We hence propose to do causal inference by stochastic complexity. That is, we propose to approximate Kolmogorov complexity via the Minimum Description Length (MDL) principle, using a score that is mini-max optimal with regard to the model class under consideration. This means that even in an adversarial setting, such as when the true distribution is not in this class, we still obtain the optimal encoding for the data relative to the class. We instantiate this framework, which we call CISC, for pairs of univariate discrete variables, using the class of multinomial distributions. Experiments show that CISC is highly accurate on synthetic, benchmark, as well as real-world data, outperforming the state of the art by a margin, and scales extremely well with regard to sample and domain sizes. %K Computer Science, Learning, cs.LG,Computer Science, Artificial Intelligence, cs.AI
[20]
K. Budhathoki and J. Vreeken, “Causal Inference by Compression,” in 16th IEEE International Conference on Data Mining (ICDM 2016), Barcelona, Spain, 2017.
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@inproceedings{budhathoki:16:origo, TITLE = {Causal Inference by Compression}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5090-5473-2}, DOI = {10.1109/ICDM.2016.0015}, PUBLISHER = {IEEE}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {16th IEEE International Conference on Data Mining (ICDM 2016)}, EDITOR = {Bonchi, Francesco and Domingo-Ferrer, Josep and Baeza-Yates, Ricardo and Zhou, Zhi-Hua and Wu, Xindong}, PAGES = {41--50}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference by Compression : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1CC0-6 %R 10.1109/ICDM.2016.0015 %D 2017 %8 02.02.2017 %B 16th International Conference on Data Mining %Z date of event: 2016-12-12 - 2016-12-15 %C Barcelona, Spain %B 16th IEEE International Conference on Data Mining %E Bonchi, Francesco; Domingo-Ferrer, Josep; Baeza-Yates, Ricardo; Zhou, Zhi-Hua; Wu, Xindong %P 41 - 50 %I IEEE %@ 978-1-5090-5473-2
[21]
K. Budhathoki and J. Vreeken, “MDL for Causal Inference on Discrete Data,” in 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, LA, USA, 2017.
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@inproceedings{BudhathokiICDM2017, TITLE = {{MDL} for Causal Inference on Discrete Data}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5386-3835-4}, DOI = {10.1109/ICDM.2017.87}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining (ICDM 2017)}, PAGES = {751--756}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T MDL for Causal Inference on Discrete Data : %G eng %U http://hdl.handle.net/21.11116/0000-0000-6458-D %R 10.1109/ICDM.2017.87 %D 2017 %B 17th IEEE International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining %P 751 - 756 %I IEEE %@ 978-1-5386-3835-4
[22]
C. X. Chu, N. Tandon, and G. Weikum, “Distilling Task Knowledge from How-To Communities,” in WWW’17, 26th International Conference on World Wide Web, Perth, Australia, 2017.
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@inproceedings{Cuong:WWW2017, TITLE = {Distilling Task Knowledge from How-To Communities}, AUTHOR = {Chu, Cuong Xuan and Tandon, Niket and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4913-0}, DOI = {10.1145/3038912.3052715}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17, 26th International Conference on World Wide Web}, PAGES = {805--814}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Chu, Cuong Xuan %A Tandon, Niket %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Distilling Task Knowledge from How-To Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-54BE-E %R 10.1145/3038912.3052715 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 %P 805 - 814 %I ACM %@ 978-1-4503-4913-0
[23]
S. Das, K. Berberich, D. Klakow, A. Mishra, and V. Setty, “Estimating Event Focus Time with Distributed Representation of Words,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
Time is an important dimension as it aids in disambiguating and understanding news- worthy events that happened in the past. It helps in chronological ordering of events to understand its causality, evolution, and ramifications. In Information Retrieval, time alongside text is known to improve the quality of search results. So, making use of the temporal dimensionality in the text-based analysis is an interesting idea to explore. Considering the importance of time, methods to automatically resolve temporal foci’s of events are essential. In this thesis, we try to solve this research question by training our models on two different kinds of corpora and then evaluate on a set of historical event-queries.
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@mastersthesis{dasmaster17, TITLE = {Estimating Event Focus Time with Distributed Representation of Words}, AUTHOR = {Das, Supratim and Berberich, Klaus and Klakow, Dietrich and Mishra, Arunav and Setty, Vinay}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Time is an important dimension as it aids in disambiguating and understanding news- worthy events that happened in the past. It helps in chronological ordering of events to understand its causality, evolution, and ramifications. In Information Retrieval, time alongside text is known to improve the quality of search results. So, making use of the temporal dimensionality in the text-based analysis is an interesting idea to explore. Considering the importance of time, methods to automatically resolve temporal foci{\textquoteright}s of events are essential. In this thesis, we try to solve this research question by training our models on two different kinds of corpora and then evaluate on a set of historical event-queries.}, }
Endnote
%0 Thesis %A Das, Supratim %A Berberich, Klaus %A Klakow, Dietrich %A Mishra, Arunav %A Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Estimating Event Focus Time with Distributed Representation of Words : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-DFF1-7 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P 83 p. %V master %9 master %X Time is an important dimension as it aids in disambiguating and understanding news- worthy events that happened in the past. It helps in chronological ordering of events to understand its causality, evolution, and ramifications. In Information Retrieval, time alongside text is known to improve the quality of search results. So, making use of the temporal dimensionality in the text-based analysis is an interesting idea to explore. Considering the importance of time, methods to automatically resolve temporal foci’s of events are essential. In this thesis, we try to solve this research question by training our models on two different kinds of corpora and then evaluate on a set of historical event-queries.
[24]
S. Das, A. Mishra, K. Berberich, and V. Setty, “Estimating Event Focus Time Using Neural Word Embeddings,” in CIKM’17, 26th ACM International Conference on Information and Knowledge Management, Singapore, Singapore, 2017.
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@inproceedings{Das_CIKM2017, TITLE = {Estimating Event Focus Time Using Neural Word Embeddings}, AUTHOR = {Das, Supratim and Mishra, Arunav and Berberich, Klaus and Setty, Vinay}, LANGUAGE = {eng}, ISBN = {978-1-4503-4918-5}, DOI = {10.1145/3132847.3133131}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {CIKM'17, 26th ACM International Conference on Information and Knowledge Management}, PAGES = {2039--2042}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Das, Supratim %A Mishra, Arunav %A Berberich, Klaus %A Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Estimating Event Focus Time Using Neural Word Embeddings : %G eng %U http://hdl.handle.net/21.11116/0000-0000-635B-B %R 10.1145/3132847.3133131 %D 2017 %B 26th ACM International Conference on Information and Knowledge Management %Z date of event: 2017-11-06 - 2017-11-10 %C Singapore, Singapore %B CIKM'17 %P 2039 - 2042 %I ACM %@ 978-1-4503-4918-5
[25]
S. Dutta, “Efficient knowledge Management for Named Entities from Text,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
The evolution of search from keywords to entities has necessitated the efficient harvesting and management of entity-centric information for constructing knowledge bases catering to various applications such as semantic search, question answering, and information retrieval. The vast amounts of natural language texts available across diverse domains on the Web provide rich sources for discovering facts about named entities such as people, places, and organizations. A key challenge, in this regard, entails the need for precise identification and disambiguation of entities across documents for extraction of attributes/relations and their proper representation in knowledge bases. Additionally, the applicability of such repositories not only involves the quality and accuracy of the stored information, but also storage management and query processing efficiency. This dissertation aims to tackle the above problems by presenting efficient approaches for entity-centric knowledge acquisition from texts and its representation in knowledge repositories. This dissertation presents a robust approach for identifying text phrases pertaining to the same named entity across huge corpora, and their disambiguation to canonical entities present in a knowledge base, by using enriched semantic contexts and link validation encapsulated in a hierarchical clustering framework. This work further presents language and consistency features for classification models to compute the credibility of obtained textual facts, ensuring quality of the extracted information. Finally, an encoding algorithm, using frequent term detection and improved data locality, to represent entities for enhanced knowledge base storage and query performance is presented.
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@phdthesis{duttaphd17, TITLE = {Efficient knowledge Management for Named Entities from Text}, AUTHOR = {Dutta, Sourav}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-67924}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {The evolution of search from keywords to entities has necessitated the efficient harvesting and management of entity-centric information for constructing knowledge bases catering to various applications such as semantic search, question answering, and information retrieval. The vast amounts of natural language texts available across diverse domains on the Web provide rich sources for discovering facts about named entities such as people, places, and organizations. A key challenge, in this regard, entails the need for precise identification and disambiguation of entities across documents for extraction of attributes/relations and their proper representation in knowledge bases. Additionally, the applicability of such repositories not only involves the quality and accuracy of the stored information, but also storage management and query processing efficiency. This dissertation aims to tackle the above problems by presenting efficient approaches for entity-centric knowledge acquisition from texts and its representation in knowledge repositories. This dissertation presents a robust approach for identifying text phrases pertaining to the same named entity across huge corpora, and their disambiguation to canonical entities present in a knowledge base, by using enriched semantic contexts and link validation encapsulated in a hierarchical clustering framework. This work further presents language and consistency features for classification models to compute the credibility of obtained textual facts, ensuring quality of the extracted information. Finally, an encoding algorithm, using frequent term detection and improved data locality, to represent entities for enhanced knowledge base storage and query performance is presented.}, }
Endnote
%0 Thesis %A Dutta, Sourav %Y Weikum, Gerhard %A referee: Nejdl, Wolfgang %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficient knowledge Management for Named Entities from Text : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-A793-E %U urn:nbn:de:bsz:291-scidok-67924 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P xv, 134 p. %V phd %9 phd %X The evolution of search from keywords to entities has necessitated the efficient harvesting and management of entity-centric information for constructing knowledge bases catering to various applications such as semantic search, question answering, and information retrieval. The vast amounts of natural language texts available across diverse domains on the Web provide rich sources for discovering facts about named entities such as people, places, and organizations. A key challenge, in this regard, entails the need for precise identification and disambiguation of entities across documents for extraction of attributes/relations and their proper representation in knowledge bases. Additionally, the applicability of such repositories not only involves the quality and accuracy of the stored information, but also storage management and query processing efficiency. This dissertation aims to tackle the above problems by presenting efficient approaches for entity-centric knowledge acquisition from texts and its representation in knowledge repositories. This dissertation presents a robust approach for identifying text phrases pertaining to the same named entity across huge corpora, and their disambiguation to canonical entities present in a knowledge base, by using enriched semantic contexts and link validation encapsulated in a hierarchical clustering framework. This work further presents language and consistency features for classification models to compute the credibility of obtained textual facts, ensuring quality of the extracted information. Finally, an encoding algorithm, using frequent term detection and improved data locality, to represent entities for enhanced knowledge base storage and query performance is presented. %U http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=dehttp://scidok.sulb.uni-saarland.de/volltexte/2017/6792/
[26]
S. Eslami, A. J. Biega, R. S. Roy, and G. Weikum, “Privacy of Hidden Profiles: Utility-Preserving Profile Removal in Online Forums,” in CIKM’17, 26th ACM International Conference on Information and Knowledge Management, Singapore, Singapore, 2017.
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@inproceedings{Eslami_CIKM2017, TITLE = {Privacy of Hidden Profiles: {U}tility-Preserving Profile Removal in Online Forums}, AUTHOR = {Eslami, Sedigheh and Biega, Asia J. and Roy, Rishiraj Saha and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4918-5}, DOI = {10.1145/3132847.3133140}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {CIKM'17, 26th ACM International Conference on Information and Knowledge Management}, PAGES = {2063--2066}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Eslami, Sedigheh %A Biega, Asia J. %A Roy, Rishiraj Saha %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Privacy of Hidden Profiles: Utility-Preserving Profile Removal in Online Forums : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3BA2-7 %R 10.1145/3132847.3133140 %D 2017 %B 26th ACM International Conference on Information and Knowledge Management %Z date of event: 2017-11-06 - 2017-11-10 %C Singapore, Singapore %B CIKM'17 %P 2063 - 2066 %I ACM %@ 978-1-4503-4918-5
[27]
S. Eslami, “Utility-preserving Profile Removal in Online Forums,” Universität des Saarlandes, Saarbrücken, 2017.
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@mastersthesis{EslamiMSc2017, TITLE = {Utility-preserving Profile Removal in Online Forums}, AUTHOR = {Eslami, Sedigheh}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Thesis %A Eslami, Sedigheh %Y Weikum, Gerhard %A referee: Roy, Rishiraj Saha %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Utility-preserving Profile Removal in Online Forums : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-9236-4 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P XII, 66 p. %V master %9 master
[28]
E. Galbrun and P. Miettinen, Redescription Mining. Cham: Springer International, 2017.
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@book{galbrun18redescription, TITLE = {Redescription Mining}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-3-319-72889-6}, DOI = {10.1007/978-3-319-72889-6}, PUBLISHER = {Springer International}, ADDRESS = {Cham}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Book %A Galbrun, Esther %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Redescription Mining : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-90D3-1 %R 10.1007/978-3-319-72889-6 %@ 978-3-319-72889-6 %I Springer International %C Cham %D 2017
[29]
E. Galbrun and P. Miettinen, “Analysing Political Opinions Using Redescription Mining,” in 16th IEEE International Conference on Data Mining Workshops (ICDMW 2016), Barcelona, Spain, 2017.
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@inproceedings{galbrun16analysing, TITLE = {Analysing Political Opinions Using Redescription Mining}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-5090-5910-2}, DOI = {10.1109/ICDMW.2016.121}, PUBLISHER = {IEEE}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {16th IEEE International Conference on Data Mining Workshops (ICDMW 2016)}, EDITOR = {Domeniconi, Carlotta and Gullo, Francesco and Bonchi, Francesco and Domingo-Ferrer, Josep and Baeza-Yates, Ricardo and Zhou, Zhi-Hua and Wu, Xindong}, PAGES = {422--427}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Galbrun, Esther %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Analysing Political Opinions Using Redescription Mining : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2247-5 %R 10.1109/ICDMW.2016.121 %D 2017 %8 02.02.2017 %B 16th International Conference on Data Mining %Z date of event: 2015-12-12 - 2015-12-15 %C Barcelona, Spain %B 16th IEEE International Conference on Data Mining Workshops %E Domeniconi, Carlotta; Gullo, Francesco; Bonchi, Francesco; Domingo-Ferrer, Josep; Baeza-Yates, Ricardo; Zhou, Zhi-Hua; Wu, Xindong %P 422 - 427 %I IEEE %@ 978-1-5090-5910-2
[30]
K. Gashteovski, R. Gemulla, and L. Del Corro, “MinIE: Minimizing Facts in Open Information Extraction,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, 2017.
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@inproceedings{DBLP:conf/emnlp/GashteovskiGC17, TITLE = {{MinIE}: {M}inimizing Facts in Open Information Extraction}, AUTHOR = {Gashteovski, Kiril and Gemulla, Rainer and Del Corro, Luciano}, LANGUAGE = {eng}, ISBN = {978-1-945626-83-8}, URL = {http://aclanthology.info/papers/D17-1277/d17-1277}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, PAGES = {2620--2630}, ADDRESS = {Copenhagen, Denmark}, }
Endnote
%0 Conference Proceedings %A Gashteovski, Kiril %A Gemulla, Rainer %A Del Corro, Luciano %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T MinIE: Minimizing Facts in Open Information Extraction : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-30F4-2 %U http://aclanthology.info/papers/D17-1277/d17-1277 %D 2017 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2017-09-09 - 2017-09-11 %C Copenhagen, Denmark %B The Conference on Empirical Methods in Natural Language Processing %P 2620 - 2630 %I ACL %@ 978-1-945626-83-8 %U http://www.aclweb.org/anthology/D17-1277
[31]
X. Ge, A. Daphalapurkar, M. Shmipi, K. Darpun, K. Pelechrinis, P. K. Chrysanthis, and D. Zeinalipour-Yazti, “Data-driven Serendipity Navigation in Urban Places,” in IEEE 37th International Conference on Distributed Computing Systems (ICDCS 2017), Atlanta, GA, USA, 2017.
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@inproceedings{icdcs17-serendipity-demo, TITLE = {Data-driven Serendipity Navigation in Urban Places}, AUTHOR = {Ge, Xiaoyi and Daphalapurkar, Ameya and Shmipi, Manali and Darpun, Kohli and Pelechrinis, Konstantinos and Chrysanthis, Panos K. and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-1-5386-1792-2}, DOI = {10.1109/ICDCS.2017.286}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {IEEE 37th International Conference on Distributed Computing Systems (ICDCS 2017)}, EDITOR = {Lee, Kisung and Liu, Ling}, PAGES = {2501--2504}, ADDRESS = {Atlanta, GA, USA}, }
Endnote
%0 Conference Proceedings %A Ge, Xiaoyi %A Daphalapurkar, Ameya %A Shmipi, Manali %A Darpun, Kohli %A Pelechrinis, Konstantinos %A Chrysanthis, Panos K. %A Zeinalipour-Yazti, Demetrios %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Data-driven Serendipity Navigation in Urban Places : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-082B-7 %R 10.1109/ICDCS.2017.286 %D 2017 %B 37th IEEE International Conference on Distributed Computing Systems %Z date of event: 2017-06-05 - 2017-06-08 %C Atlanta, GA, USA %B IEEE 37th International Conference on Distributed Computing Systems %E Lee, Kisung; Liu, Ling %P 2501 - 2504 %I IEEE %@ 978-1-5386-1792-2
[32]
B. Goldsmith, M. Boley, J. Vreeken, M. Scheffler, and L. Ghiringhelli,, “Uncovering Structure-property Relationships of Materials by Subgroup Discovery,” New Journal of Physics, vol. 19, no. 1, 2017.
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@article{goldsmith:17:gold, TITLE = {Uncovering Structure-property Relationships of Materials by Subgroup Discovery}, AUTHOR = {Goldsmith, Brian and Boley, Mario and Vreeken, Jilles and Scheffler, Matthias and Ghiringhelli,, Luca}, LANGUAGE = {eng}, ISSN = {1367-2630}, DOI = {10.1088/1367-2630/aa57c2}, PUBLISHER = {IOP Publishing}, ADDRESS = {Bristol}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {New Journal of Physics}, VOLUME = {19}, NUMBER = {1}, EID = {013031}, }
Endnote
%0 Journal Article %A Goldsmith, Brian %A Boley, Mario %A Vreeken, Jilles %A Scheffler, Matthias %A Ghiringhelli,, Luca %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Uncovering Structure-property Relationships of Materials by Subgroup Discovery : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4BF5-4 %R 10.1088/1367-2630/aa57c2 %7 2017 %D 2017 %J New Journal of Physics %O New J. Phys. %V 19 %N 1 %Z sequence number: 013031 %I IOP Publishing %C Bristol %@ false %U http://iopscience.iop.org/article/10.1088/1367-2630/aa57c2
[33]
A. Grycner, “Constructing Lexicons of Relational Phrases,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
Knowledge Bases are one of the key components of Natural Language Understanding systems. For example, DBpedia, YAGO, and Wikidata capture and organize knowledge about named entities and relations between them, which is often crucial for tasks like Question Answering and Named Entity Disambiguation. While Knowledge Bases have good coverage of prominent entities, they are often limited with respect to relations. The goal of this thesis is to bridge this gap and automatically create lexicons of textual representations of relations, namely relational phrases. The lexicons should contain information about paraphrases, hierarchy, as well as semantic types of arguments of relational phrases. The thesis makes three main contributions. The first contribution addresses disambiguating relational phrases by aligning them with the WordNet dictionary. Moreover, the alignment allows imposing the WordNet hierarchy on the relational phrases. The second contribution proposes a method for graph construction of relations using Probabilistic Graphical Models. In addition, we apply this model to relation paraphrasing. The third contribution presents a method for constructing a lexicon of relational paraphrases with fine-grained semantic typing of arguments. This method is based on information from a multilingual parallel corpus.
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@phdthesis{Grynerphd17, TITLE = {Constructing Lexicons of Relational Phrases}, AUTHOR = {Grycner, Adam}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-69101}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Knowledge Bases are one of the key components of Natural Language Understanding systems. For example, DBpedia, YAGO, and Wikidata capture and organize knowledge about named entities and relations between them, which is often crucial for tasks like Question Answering and Named Entity Disambiguation. While Knowledge Bases have good coverage of prominent entities, they are often limited with respect to relations. The goal of this thesis is to bridge this gap and automatically create lexicons of textual representations of relations, namely relational phrases. The lexicons should contain information about paraphrases, hierarchy, as well as semantic types of arguments of relational phrases. The thesis makes three main contributions. The first contribution addresses disambiguating relational phrases by aligning them with the WordNet dictionary. Moreover, the alignment allows imposing the WordNet hierarchy on the relational phrases. The second contribution proposes a method for graph construction of relations using Probabilistic Graphical Models. In addition, we apply this model to relation paraphrasing. The third contribution presents a method for constructing a lexicon of relational paraphrases with fine-grained semantic typing of arguments. This method is based on information from a multilingual parallel corpus.}, }
Endnote
%0 Thesis %A Grycner, Adam %Y Weikum, Gerhard %A referee: Klakow, Dietrich %A referee: Ponzetto, Simone Paolo %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Constructing Lexicons of Relational Phrases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-933B-1 %U urn:nbn:de:bsz:291-scidok-69101 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P 125 p. %V phd %9 phd %X Knowledge Bases are one of the key components of Natural Language Understanding systems. For example, DBpedia, YAGO, and Wikidata capture and organize knowledge about named entities and relations between them, which is often crucial for tasks like Question Answering and Named Entity Disambiguation. While Knowledge Bases have good coverage of prominent entities, they are often limited with respect to relations. The goal of this thesis is to bridge this gap and automatically create lexicons of textual representations of relations, namely relational phrases. The lexicons should contain information about paraphrases, hierarchy, as well as semantic types of arguments of relational phrases. The thesis makes three main contributions. The first contribution addresses disambiguating relational phrases by aligning them with the WordNet dictionary. Moreover, the alignment allows imposing the WordNet hierarchy on the relational phrases. The second contribution proposes a method for graph construction of relations using Probabilistic Graphical Models. In addition, we apply this model to relation paraphrasing. The third contribution presents a method for constructing a lexicon of relational paraphrases with fine-grained semantic typing of arguments. This method is based on information from a multilingual parallel corpus. %U http://scidok.sulb.uni-saarland.de/volltexte/2017/6910/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[34]
A. Guimarães, L. Wang, and G. Weikum, “Us and Them: Adversarial Politics on Twitter,” in 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017 ), New Orleans, LA, USA, 2017.
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@inproceedings{Guimaraes_ICDMW2017, TITLE = {Us and Them: {A}dversarial Politics on {Twitter}}, AUTHOR = {Guimar{\~a}es, Anna and Wang, Liqiang and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-5386-1480-8}, DOI = {10.1109/ICDMW.2017.119}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining Workshops (ICDMW 2017 )}, EDITOR = {Gottumukkala, Raju and Ning, Xia and Dong, Guozhu and Raghavan, Vijav and Aluru, Srinivas and Karypis, George and Miele, Lucio and Wu, Xindong}, PAGES = {872--877}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Guimarães, Anna %A Wang, Liqiang %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Us and Them: Adversarial Politics on Twitter : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3B89-4 %R 10.1109/ICDMW.2017.119 %D 2017 %B 17th International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining Workshops %E Gottumukkala, Raju; Ning, Xia; Dong, Guozhu; Raghavan, Vijav; Aluru, Srinivas; Karypis, George; Miele, Lucio; Wu, Xindong %P 872 - 877 %I IEEE %@ 978-1-5386-1480-8
[35]
D. Gupta, K. Berberich, J. Strötgen, and D. Zeinalipour-Yazti, “Generating Semantic Aspects for Queries,” Max-Planck-Institut für Informatik, Saarbrücken, MPI–I–2017–5-001, 2017.
Abstract
Ambiguous information needs expressed in a limited number of keywords often result in long-winded query sessions and many query reformulations. In this work, we tackle ambiguous queries by providing automatically gen- erated semantic aspects that can guide users to satisfying results regarding their information needs. To generate semantic aspects, we use semantic an- notations available in the documents and leverage models representing the semantic relationships between annotations of the same type. The aspects in turn provide us a foundation for representing text in a completely structured manner, thereby allowing for a semantically-motivated organization of search results. We evaluate our approach on a testbed of over 5,000 aspects on Web scale document collections amounting to more than 450 million documents, with temporal, geographic, and named entity annotations as example dimen- sions. Our experimental results show that our general approach is Web-scale ready and finds relevant aspects for highly ambiguous queries.
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@techreport{Guptareport2007, TITLE = {Generating Semantic Aspects for Queries}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus and Str{\"o}tgen, Jannik and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISSN = {0946-011X}, NUMBER = {MPI–I–2017–5-001}, INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Ambiguous information needs expressed in a limited number of keywords often result in long-winded query sessions and many query reformulations. In this work, we tackle ambiguous queries by providing automatically gen- erated semantic aspects that can guide users to satisfying results regarding their information needs. To generate semantic aspects, we use semantic an- notations available in the documents and leverage models representing the semantic relationships between annotations of the same type. The aspects in turn provide us a foundation for representing text in a completely structured manner, thereby allowing for a semantically-motivated organization of search results. We evaluate our approach on a testbed of over 5,000 aspects on Web scale document collections amounting to more than 450 million documents, with temporal, geographic, and named entity annotations as example dimen- sions. Our experimental results show that our general approach is Web-scale ready and finds relevant aspects for highly ambiguous queries.}, TYPE = {Research Report}, }
Endnote
%0 Report %A Gupta, Dhruv %A Berberich, Klaus %A Strötgen, Jannik %A Zeinalipour-Yazti, Demetrios %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Generating Semantic Aspects for Queries : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-07DD-0 %Y Max-Planck-Institut für Informatik %C Saarbrücken %D 2017 %P 39 p. %X Ambiguous information needs expressed in a limited number of keywords often result in long-winded query sessions and many query reformulations. In this work, we tackle ambiguous queries by providing automatically gen- erated semantic aspects that can guide users to satisfying results regarding their information needs. To generate semantic aspects, we use semantic an- notations available in the documents and leverage models representing the semantic relationships between annotations of the same type. The aspects in turn provide us a foundation for representing text in a completely structured manner, thereby allowing for a semantically-motivated organization of search results. We evaluate our approach on a testbed of over 5,000 aspects on Web scale document collections amounting to more than 450 million documents, with temporal, geographic, and named entity annotations as example dimen- sions. Our experimental results show that our general approach is Web-scale ready and finds relevant aspects for highly ambiguous queries. %B Research Report %@ false
[36]
S. Gurajada, “Distributed Querying of Large Labeled Graphs,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
Graph is a vital abstract data type that has profound significance in several applications. Because of its versitality, graphs have been adapted into several different forms and one such adaption with many practical applications is the “Labeled Graph”, where vertices and edges are labeled. An enormous research effort has been invested in to the task of managing and querying graphs, yet a lot challenges are left unsolved. In this thesis, we advance the state-of-the-art for the following query models, and propose a distributed solution to process them in an efficient and scalable manner. • Set Reachability. We formalize and investigate a generalization of the basic notion of reachability, called set reachability. Set reachability deals with finding all reachable pairs for a given source and target sets. We present a non-iterative distributed solution that takes only a single round of communication for any set reachability query. This is achieved by precomputation, replication, and indexing of partial reachabilities among the boundary vertices. • Basic Graph Patterns (BGP). Supported by majority of query languages, BGP queries are a common mode of querying knowledge graphs, biological datasets, etc. We present a novel distributed architecture that relies on the concepts of asynchronous executions, join-ahead pruning, and a multi-threaded query processing framework to process BGP queries in an efficient and scalable manner. • Generalized Graph Patterns (GGP). These queries combine the semantics of pattern matching and navigational queries, and are popular in scenarios where the schema of an underlying graph is either unknown or partially known. We present a distributed solution with bimodal indexing layout that individually support efficient processing of BGP queries and navigational queries. Furthermore, we design a unified query optimizer and a processor to efficiently process GGP queries and also in a scalable manner. To this end, we propose a prototype distributed engine, coined “TriAD” (Triple Asynchronous and Distributed) that supports all the aforementioned query models. We also provide a detailed empirical evaluation of TriAD in comparison to several state-of-the-art systems over multiple real-world and synthetic datasets.
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@phdthesis{guraphd2017, TITLE = {Distributed Querying of Large Labeled Graphs}, AUTHOR = {Gurajada, Sairam}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-67738}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Graph is a vital abstract data type that has profound significance in several applications. Because of its versitality, graphs have been adapted into several different forms and one such adaption with many practical applications is the {\textquotedblleft}Labeled Graph{\textquotedblright}, where vertices and edges are labeled. An enormous research effort has been invested in to the task of managing and querying graphs, yet a lot challenges are left unsolved. In this thesis, we advance the state-of-the-art for the following query models, and propose a distributed solution to process them in an efficient and scalable manner. \mbox{$\bullet$} Set Reachability. We formalize and investigate a generalization of the basic notion of reachability, called set reachability. Set reachability deals with finding all reachable pairs for a given source and target sets. We present a non-iterative distributed solution that takes only a single round of communication for any set reachability query. This is achieved by precomputation, replication, and indexing of partial reachabilities among the boundary vertices. \mbox{$\bullet$} Basic Graph Patterns (BGP). Supported by majority of query languages, BGP queries are a common mode of querying knowledge graphs, biological datasets, etc. We present a novel distributed architecture that relies on the concepts of asynchronous executions, join-ahead pruning, and a multi-threaded query processing framework to process BGP queries in an efficient and scalable manner. \mbox{$\bullet$} Generalized Graph Patterns (GGP). These queries combine the semantics of pattern matching and navigational queries, and are popular in scenarios where the schema of an underlying graph is either unknown or partially known. We present a distributed solution with bimodal indexing layout that individually support efficient processing of BGP queries and navigational queries. Furthermore, we design a unified query optimizer and a processor to efficiently process GGP queries and also in a scalable manner. To this end, we propose a prototype distributed engine, coined {\textquotedblleft}TriAD{\textquotedblright} (Triple Asynchronous and Distributed) that supports all the aforementioned query models. We also provide a detailed empirical evaluation of TriAD in comparison to several state-of-the-art systems over multiple real-world and synthetic datasets.}, }
Endnote
%0 Thesis %A Gurajada, Sairam %Y Theobald, Martin %A referee: Weikum, Gerhard %A referee: Özsu, M. Tamer %A referee: Michel, Sebastian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Distributed Querying of Large Labeled Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-8202-E %U urn:nbn:de:bsz:291-scidok-67738 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P x, 167 p. %V phd %9 phd %X Graph is a vital abstract data type that has profound significance in several applications. Because of its versitality, graphs have been adapted into several different forms and one such adaption with many practical applications is the “Labeled Graph”, where vertices and edges are labeled. An enormous research effort has been invested in to the task of managing and querying graphs, yet a lot challenges are left unsolved. In this thesis, we advance the state-of-the-art for the following query models, and propose a distributed solution to process them in an efficient and scalable manner. • Set Reachability. We formalize and investigate a generalization of the basic notion of reachability, called set reachability. Set reachability deals with finding all reachable pairs for a given source and target sets. We present a non-iterative distributed solution that takes only a single round of communication for any set reachability query. This is achieved by precomputation, replication, and indexing of partial reachabilities among the boundary vertices. • Basic Graph Patterns (BGP). Supported by majority of query languages, BGP queries are a common mode of querying knowledge graphs, biological datasets, etc. We present a novel distributed architecture that relies on the concepts of asynchronous executions, join-ahead pruning, and a multi-threaded query processing framework to process BGP queries in an efficient and scalable manner. • Generalized Graph Patterns (GGP). These queries combine the semantics of pattern matching and navigational queries, and are popular in scenarios where the schema of an underlying graph is either unknown or partially known. We present a distributed solution with bimodal indexing layout that individually support efficient processing of BGP queries and navigational queries. Furthermore, we design a unified query optimizer and a processor to efficiently process GGP queries and also in a scalable manner. To this end, we propose a prototype distributed engine, coined “TriAD” (Triple Asynchronous and Distributed) that supports all the aforementioned query models. We also provide a detailed empirical evaluation of TriAD in comparison to several state-of-the-art systems over multiple real-world and synthetic datasets. %U http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=dehttp://scidok.sulb.uni-saarland.de/volltexte/2017/6773/
[37]
K. Hui, “Automatic Methods for Low-Cost Evaluation and Position-Aware Models for Neural Information Retrieva,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
An information retrieval (IR) system assists people in consuming huge amount of data, where the evaluation and the construction of such systems are important. However, there exist two difficulties: the overwhelmingly large number of query-document pairs to judge, making IR evaluation a manually laborious task; and the complicated patterns to model due to the non-symmetric, heterogeneous relationships between a query-document pair, where different interaction patterns such as term dependency and proximity have been demonstrated to be useful, yet are non-trivial for a single IR model to encode. In this thesis we attempt to address both difficulties from the perspectives of IR evaluation and of the retrieval model respectively, by reducing the manual cost with automatic methods, by investigating the usage of crowdsourcing in collecting preference judgments, and by proposing novel neural retrieval models. In particular, to address the large number of query-document pairs in IR evaluation, a low-cost selective labeling method is proposed to pick out a small subset of representative documents for manual judgments in favor of the follow-up prediction for the remaining query-document pairs; furthermore, a language-model based cascade measure framework is developed to evaluate the novelty and diversity, utilizing the content of the labeled documents to mitigate incomplete labels. In addition, we also attempt to make the preference judgments practically usable by empirically investigating different properties of the judgments when collected via crowdsourcing; and by proposing a novel judgment mechanism, making a compromise between the judgment quality and the number of judgments. Finally, to model different complicated patterns in a single retrieval model, inspired by the recent advances in deep learning, we develop novel neural IR models to incorporate different patterns like term dependency, query proximity, density of relevance, and query coverage in a single model. We demonstrate their superior performances through evaluations on different datasets.
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@phdthesis{HUiphd2017, TITLE = {Automatic Methods for Low-Cost Evaluation and Position-Aware Models for Neural Information Retrieva}, AUTHOR = {Hui, Kai}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-269423}, DOI = {10.22028/D291-26942}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {An information retrieval (IR) system assists people in consuming huge amount of data, where the evaluation and the construction of such systems are important. However, there exist two difficulties: the overwhelmingly large number of query-document pairs to judge, making IR evaluation a manually laborious task; and the complicated patterns to model due to the non-symmetric, heterogeneous relationships between a query-document pair, where different interaction patterns such as term dependency and proximity have been demonstrated to be useful, yet are non-trivial for a single IR model to encode. In this thesis we attempt to address both difficulties from the perspectives of IR evaluation and of the retrieval model respectively, by reducing the manual cost with automatic methods, by investigating the usage of crowdsourcing in collecting preference judgments, and by proposing novel neural retrieval models. In particular, to address the large number of query-document pairs in IR evaluation, a low-cost selective labeling method is proposed to pick out a small subset of representative documents for manual judgments in favor of the follow-up prediction for the remaining query-document pairs; furthermore, a language-model based cascade measure framework is developed to evaluate the novelty and diversity, utilizing the content of the labeled documents to mitigate incomplete labels. In addition, we also attempt to make the preference judgments practically usable by empirically investigating different properties of the judgments when collected via crowdsourcing; and by proposing a novel judgment mechanism, making a compromise between the judgment quality and the number of judgments. Finally, to model different complicated patterns in a single retrieval model, inspired by the recent advances in deep learning, we develop novel neural IR models to incorporate different patterns like term dependency, query proximity, density of relevance, and query coverage in a single model. We demonstrate their superior performances through evaluations on different datasets.}, }
Endnote
%0 Thesis %A Hui, Kai %Y Berberich, Klaus %A referee: Weikum, Gerhard %A referee: Dietz, Laura %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Automatic Methods for Low-Cost Evaluation and Position-Aware Models for Neural Information Retrieva : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-8921-E %U urn:nbn:de:bsz:291-scidok-ds-269423 %R 10.22028/D291-26942 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P xiv, 130 p. %V phd %9 phd %X An information retrieval (IR) system assists people in consuming huge amount of data, where the evaluation and the construction of such systems are important. However, there exist two difficulties: the overwhelmingly large number of query-document pairs to judge, making IR evaluation a manually laborious task; and the complicated patterns to model due to the non-symmetric, heterogeneous relationships between a query-document pair, where different interaction patterns such as term dependency and proximity have been demonstrated to be useful, yet are non-trivial for a single IR model to encode. In this thesis we attempt to address both difficulties from the perspectives of IR evaluation and of the retrieval model respectively, by reducing the manual cost with automatic methods, by investigating the usage of crowdsourcing in collecting preference judgments, and by proposing novel neural retrieval models. In particular, to address the large number of query-document pairs in IR evaluation, a low-cost selective labeling method is proposed to pick out a small subset of representative documents for manual judgments in favor of the follow-up prediction for the remaining query-document pairs; furthermore, a language-model based cascade measure framework is developed to evaluate the novelty and diversity, utilizing the content of the labeled documents to mitigate incomplete labels. In addition, we also attempt to make the preference judgments practically usable by empirically investigating different properties of the judgments when collected via crowdsourcing; and by proposing a novel judgment mechanism, making a compromise between the judgment quality and the number of judgments. Finally, to model different complicated patterns in a single retrieval model, inspired by the recent advances in deep learning, we develop novel neural IR models to incorporate different patterns like term dependency, query proximity, density of relevance, and query coverage in a single model. We demonstrate their superior performances through evaluations on different datasets. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26894
[38]
K. Hui and K. Berberich, “Merge-Tie-Judge: Low-Cost Preference Judgments with Ties,” in ICTIR’17, 7th International Conference on the Theory of Information Retrieval, Amsterdam, The Netherlands, 2017.
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@inproceedings{HuiICTIR2017b, TITLE = {{Merge-Tie-Judge}: Low-Cost Preference Judgments with Ties}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-4490-6}, DOI = {10.1145/3121050.3121095}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {ICTIR'17, 7th International Conference on the Theory of Information Retrieval}, PAGES = {277--280}, ADDRESS = {Amsterdam, The Netherlands}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Merge-Tie-Judge: Low-Cost Preference Judgments with Ties : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-064B-2 %R 10.1145/3121050.3121095 %D 2017 %B 7th International Conference on the Theory of Information Retrieval %Z date of event: 2017-10-01 - 2017-10-04 %C Amsterdam, The Netherlands %B ICTIR'17 %P 277 - 280 %I ACM %@ 978-1-4503-4490-6
[39]
K. Hui, K. Berberich, and I. Mele, “Dealing with Incomplete Judgments in Cascade Measures,” in ICTIR’17, 7th International Conference on the Theory of Information Retrieval, Amsterdam, The Netherlands, 2017.
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@inproceedings{HuiICTIR2017, TITLE = {Dealing with Incomplete Judgments in Cascade Measures}, AUTHOR = {Hui, Kai and Berberich, Klaus and Mele, Ida}, LANGUAGE = {eng}, ISBN = {978-1-4503-4490-6}, DOI = {10.1145/3121050.3121064}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {ICTIR'17, 7th International Conference on the Theory of Information Retrieval}, PAGES = {83--90}, ADDRESS = {Amsterdam, The Netherlands}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %A Mele, Ida %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Dealing with Incomplete Judgments in Cascade Measures : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-0649-6 %R 10.1145/3121050.3121064 %D 2017 %B 7th International Conference on the Theory of Information Retrieval %Z date of event: 2017-10-01 - 2017-10-04 %C Amsterdam, The Netherlands %B ICTIR'17 %P 83 - 90 %I ACM %@ 978-1-4503-4490-6
[40]
K. Hui and K. Berberich, “Low-Cost Preference Judgment via Ties,” in Advances in Information Retrieval (ECIR 2017), Aberdeen, UK, 2017.
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@inproceedings{hui2017short, TITLE = {Low-Cost Preference Judgment via Ties}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-56607-8}, DOI = {10.1007/978-3-319-56608-5_58}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2017)}, EDITOR = {Jose, Joemon M. and Hauff, Claudia and Altingovde, Ismail Sengor and Song, Dawei and Albakour, Dyaa and Watt, Stuart and Tait, John}, PAGES = {626--632}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10193}, ADDRESS = {Aberdeen, UK}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Low-Cost Preference Judgment via Ties : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1F7B-A %R 10.1007/978-3-319-56608-5_58 %D 2017 %B 39th European Conference on Information Retrieval %Z date of event: 2017-04-09 - 2017-04-13 %C Aberdeen, UK %B Advances in Information Retrieval %E Jose, Joemon M.; Hauff, Claudia; Altingovde, Ismail Sengor; Song, Dawei; Albakour, Dyaa; Watt, Stuart; Tait, John %P 626 - 632 %I Springer %@ 978-3-319-56607-8 %B Lecture Notes in Computer Science %N 10193
[41]
K. Hui and K. Berberich, “Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing,” in Advances in Information Retrieval (ECIR 2017), Aberdeen, UK, 2017.
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@inproceedings{hui2017full, TITLE = {Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-56607-8}, DOI = {10.1007/978-3-319-56608-5_19}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2017)}, EDITOR = {Jose, Joemon M. and Hauff, Claudia and Altingovde, Ismail Sengor and Song, Dawei and Albakour, Dyaa and Watt, Stuart and Tait, John}, PAGES = {239--251}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10193}, ADDRESS = {Aberdeen, UK}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1F75-5 %R 10.1007/978-3-319-56608-5_19 %D 2017 %B 39th European Conference on Information Retrieval %Z date of event: 2016-04-09 - 2017-04-13 %C Aberdeen, UK %B Advances in Information Retrieval %E Jose, Joemon M.; Hauff, Claudia; Altingovde, Ismail Sengor; Song, Dawei; Albakour, Dyaa; Watt, Stuart; Tait, John %P 239 - 251 %I Springer %@ 978-3-319-56607-8 %B Lecture Notes in Computer Science %N 10193
[42]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “PACRR: A Position-Aware Neural IR Model for Relevance Matching,” 2017. [Online]. Available: http://arxiv.org/abs/1704.03940. (arXiv: 1704.03940)
Abstract
In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR (Position-Aware Convolutional-Recurrent Relevance), aiming at better modeling position-dependent interactions between a query and a document via convolutional layers as well as recurrent layers. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under different benchmarks.
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@online{DBLP:journals/corr/HuiYBM17, TITLE = {{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1704.03940}, EPRINT = {1704.03940}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR (Position-Aware Convolutional-Recurrent Relevance), aiming at better modeling position-dependent interactions between a query and a document via convolutional layers as well as recurrent layers. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under different benchmarks.}, }
Endnote
%0 Report %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T PACRR: A Position-Aware Neural IR Model for Relevance Matching : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90A8-3 %U http://arxiv.org/abs/1704.03940 %D 2017 %X In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR (Position-Aware Convolutional-Recurrent Relevance), aiming at better modeling position-dependent interactions between a query and a document via convolutional layers as well as recurrent layers. Extensive experiments on six years' TREC Web Track data confirm that the proposed model yields better results under different benchmarks. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[43]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “PACRR: A Position-Aware Neural IR Model for Relevance Matching,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, 2017.
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@inproceedings{HuiENMLP2017, TITLE = {{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-945626-83-8}, URL = {https://aclanthology.info/pdf/D/D17/D17-1111.pdf}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, PAGES = {1060--1069}, ADDRESS = {Copenhagen, Denmark}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T PACRR: A Position-Aware Neural IR Model for Relevance Matching : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-063F-D %U https://aclanthology.info/pdf/D/D17/D17-1111.pdf %D 2017 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2017-09-09 - 2017-09-11 %C Copenhagen, Denmark %B The Conference on Empirical Methods in Natural Language Processing %P 1060 - 1069 %I ACL %@ 978-1-945626-83-8 %U https://aclanthology.info/pdf/D/D17/D17-1111.pdf
[44]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model,” 2017. [Online]. Available: http://arxiv.org/abs/1706.10192. (arXiv: 1706.10192)
Abstract
Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results.
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@online{HuiarXiv2017b, TITLE = {{RE-PACRR}: {A} Context and Density-Aware Neural Information Retrieval Model}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1706.10192}, EPRINT = {1706.10192}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results.}, }
Endnote
%0 Report %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-064D-D %U http://arxiv.org/abs/1706.10192 %D 2017 %X Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[45]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “Position-Aware Representations for Relevance Matching in Neural Information Retrieval,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{HuiWWW2017, TITLE = {Position-Aware Representations for Relevance Matching in Neural Information Retrieval}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3054258}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {799--800}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Position-Aware Representations for Relevance Matching in Neural Information Retrieval : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90A4-B %R 10.1145/3041021.3054258 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 799 - 800 %I ACM %@ 978-1-4503-4914-7
[46]
K. Hui, A. Yates, K. Berberich, and G. de Melo, “Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval,” in WSDM’18, 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, USA, 2017.
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@inproceedings{Hui_WSDM2018, TITLE = {Co-{PACRR}: {A} Context-Aware Neural {IR} Model for Ad-hoc Retrieval}, AUTHOR = {Hui, Kai and Yates, Andrew and Berberich, Klaus and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5581-0}, DOI = {10.1145/3159652.3159689}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WSDM'18, 11th ACM International Conference on Web Search and Data Mining}, PAGES = {279--287}, ADDRESS = {Marina Del Rey, CA, USA}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Yates, Andrew %A Berberich, Klaus %A de Melo, Gerard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval : %G eng %U http://hdl.handle.net/21.11116/0000-0000-6367-D %R 10.1145/3159652.3159689 %D 2017 %B 11th ACM International Conference on Web Search and Data Mining %Z date of event: 2018-02-05 - 2018-02-09 %C Marina Del Rey, CA, USA %B WSDM'18 %P 279 - 287 %I ACM %@ 978-1-4503-5581-0
[47]
J. Kalofolias, E. Galbrun, and P. Miettinen, “From Sets of Good Redescriptions to Good Sets of Redescriptions,” in 16th IEEE International Conference on Data Mining (ICDM 2016), Barcelona, Spain, 2017.
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@inproceedings{kalofolias16from, TITLE = {From Sets of Good Redescriptions to Good Sets of Redescriptions}, AUTHOR = {Kalofolias, Janis and Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-5090-5473-2}, DOI = {10.1109/ICDM.2016.0032}, PUBLISHER = {IEEE}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {16th IEEE International Conference on Data Mining (ICDM 2016)}, PAGES = {211--220}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Kalofolias, Janis %A Galbrun, Esther %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T From Sets of Good Redescriptions to Good Sets of Redescriptions : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-224D-A %R 10.1109/ICDM.2016.0032 %D 2017 %8 02.02.2017 %B 16th International Conference on Data Mining %Z date of event: 2016-12-12 - 2016-12-15 %C Barcelona, Spain %B 16th IEEE International Conference on Data Mining %P 211 - 220 %I IEEE %@ 978-1-5090-5473-2
[48]
J. Kalofolias, M. Boley, and J. Vreeken, “Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups,” 2017. [Online]. Available: http://arxiv.org/abs/1709.07941. (arXiv: 1709.07941)
Abstract
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-$k$ subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time.
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@online{Kalofolias_arXiv2017, TITLE = {Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups}, AUTHOR = {Kalofolias, Janis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1709.07941}, EPRINT = {1709.07941}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-$k$ subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time.}, }
Endnote
%0 Report %A Kalofolias, Janis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-0685-D %U http://arxiv.org/abs/1709.07941 %D 2017 %X Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-$k$ subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time. %K Computer Science, Databases, cs.DB,Computer Science, Artificial Intelligence, cs.AI
[49]
J. Kalofolias, M. Boley, and J. Vreeken, “Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups,” in 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, LA, USA, 2017.
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@inproceedings{KalofoliasICDM2017, TITLE = {Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups}, AUTHOR = {Kalofolias, Janis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5386-3835-4}, DOI = {10.1109/ICDM.2017.29}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining (ICDM 2017)}, PAGES = {197--206}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Kalofolias, Janis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups : %G eng %U http://hdl.handle.net/21.11116/0000-0000-63C2-5 %R 10.1109/ICDM.2017.29 %D 2017 %B 17th IEEE International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining %P 197 - 206 %I IEEE %@ 978-1-5386-3835-4
[50]
S. Karaev and P. Miettinen, “Algorithms for Approximate Subtropical Matrix Factorization,” 2017. [Online]. Available: http://arxiv.org/abs/1707.08872. (arXiv: 1707.08872)
Abstract
Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for finding patterns from the data. Rather than using the standard algebra -- and the summation of the rank-1 components to build the approximation of the original matrix -- we use the subtropical algebra, which is an algebra over the nonnegative real values with the summation replaced by the maximum operator. Subtropical matrix factorizations allow "winner-takes-it-all" interpretations of the rank-1 components, revealing different structure than the normal (nonnegative) factorizations. We study the complexity and sparsity of the factorizations, and present a framework for finding low-rank subtropical factorizations. We present two specific algorithms, called Capricorn and Cancer, that are part of our framework. They can be used with data that has been corrupted with different types of noise, and with different error metrics, including the sum-of-absolute differences, Frobenius norm, and Jensen--Shannon divergence. Our experiments show that the algorithms perform well on data that has subtropical structure, and that they can find factorizations that are both sparse and easy to interpret.
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@online{Karaev_arXiv2017, TITLE = {Algorithms for Approximate Subtropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Miettinen, Pauli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1707.08872}, EPRINT = {1707.08872}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for finding patterns from the data. Rather than using the standard algebra -- and the summation of the rank-1 components to build the approximation of the original matrix -- we use the subtropical algebra, which is an algebra over the nonnegative real values with the summation replaced by the maximum operator. Subtropical matrix factorizations allow "winner-takes-it-all" interpretations of the rank-1 components, revealing different structure than the normal (nonnegative) factorizations. We study the complexity and sparsity of the factorizations, and present a framework for finding low-rank subtropical factorizations. We present two specific algorithms, called Capricorn and Cancer, that are part of our framework. They can be used with data that has been corrupted with different types of noise, and with different error metrics, including the sum-of-absolute differences, Frobenius norm, and Jensen--Shannon divergence. Our experiments show that the algorithms perform well on data that has subtropical structure, and that they can find factorizations that are both sparse and easy to interpret.}, }
Endnote
%0 Report %A Karaev, Sanjar %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Algorithms for Approximate Subtropical Matrix Factorization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-065A-F %U http://arxiv.org/abs/1707.08872 %D 2017 %X Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for finding patterns from the data. Rather than using the standard algebra -- and the summation of the rank-1 components to build the approximation of the original matrix -- we use the subtropical algebra, which is an algebra over the nonnegative real values with the summation replaced by the maximum operator. Subtropical matrix factorizations allow "winner-takes-it-all" interpretations of the rank-1 components, revealing different structure than the normal (nonnegative) factorizations. We study the complexity and sparsity of the factorizations, and present a framework for finding low-rank subtropical factorizations. We present two specific algorithms, called Capricorn and Cancer, that are part of our framework. They can be used with data that has been corrupted with different types of noise, and with different error metrics, including the sum-of-absolute differences, Frobenius norm, and Jensen--Shannon divergence. Our experiments show that the algorithms perform well on data that has subtropical structure, and that they can find factorizations that are both sparse and easy to interpret. %K Computer Science, Learning, cs.LG %U http://people.mpi-inf.mpg.de/~pmiettin/tropical/
[51]
E. Kuzey, “Populating Knowledge bases with Temporal Information,” Universität des Saarlandes, Saarbrücken, 2017.
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@phdthesis{KuzeyPhd2017, TITLE = {Populating Knowledge bases with Temporal Information}, AUTHOR = {Kuzey, Erdal}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Thesis %A Kuzey, Erdal %Y Weikum, Gerhard %A referee: de Rijke , Maarten %A referee: Suchanek, Fabian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Populating Knowledge bases with Temporal Information : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-EAE5-7 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P XIV, 143 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/volltexte/2017/6811/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[52]
L. Lange, “Time in Newspaper: A Large-Scale Analysis of Temporal Expressions in News Corpora,” Universität des Saarlandes, Saarbrücken, 2017.
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@mastersthesis{LangeBcS2017, TITLE = {Time in Newspaper: {A} Large-Scale Analysis of Temporal Expressions in News Corpora}, AUTHOR = {Lange, Lukas}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, TYPE = {Bachelor's thesis}, }
Endnote
%0 Thesis %A Lange, Lukas %Y Strötgen, Jannik %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Time in Newspaper: A Large-Scale Analysis of Temporal Expressions in News Corpora : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5D08-B %I Universität des Saarlandes %C Saarbrücken %D 2017 %P 77 p. %V bachelor %9 bachelor
[53]
F. A. Lisi and D. Stepanova, “Combining Rule Learning and Nonmonotonic Reasoning for Link Prediction in Knowledge Graphs,” in Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR, London, UK, 2017.
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@inproceedings{LisiRuleML2017, TITLE = {Combining Rule Learning and Nonmonotonic Reasoning for Link Prediction in Knowledge Graphs}, AUTHOR = {Lisi, Francesca Alessandra and Stepanova, Daria}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {urn:nbn:de:0074-1875-8}, PUBLISHER = {CEUR-WS.org}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR}, EDITOR = {Bassiliades, Nick and Bikakis, Antonis and Constantini, Stefania and Franconi, Enrico and Giurca, Adrian and Kontchakov, Roman and Patkosi, Theodore and Sadri, Fariba and Van Woensel, William}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {1875}, ADDRESS = {London, UK}, }
Endnote
%0 Conference Proceedings %A Lisi, Francesca Alessandra %A Stepanova, Daria %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Combining Rule Learning and Nonmonotonic Reasoning for Link Prediction in Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-55FC-8 %D 2017 %B International Joint Conference on Rules and Reasoning %Z date of event: 2017-07-12 - 2017-07-15 %C London, UK %B Doctoral Consortium, Challenge, Industry Track, Tutorials and Posters @ RuleML+RR %E Bassiliades, Nick; Bikakis, Antonis; Constantini, Stefania; Franconi, Enrico; Giurca, Adrian; Kontchakov, Roman; Patkosi, Theodore; Sadri, Fariba; Van Woensel, William %I CEUR-WS.org %B CEUR Workshop Proceedings %N 1875 %@ false %U http://ceur-ws.org/Vol-1875/paper20.pdf
[54]
S. MacAvaney, K. Hui, and A. Yates, “An Approach for Weakly-Supervised Deep Information Retrieval,” 2017. [Online]. Available: http://arxiv.org/abs/1707.00189. (arXiv: 1707.00189)
Abstract
Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that---given a weak training set of pseudo-queries, documents, relevance information---filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article's headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection.
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@online{MacAvaney_arXiv2017, TITLE = {An Approach for Weakly-Supervised Deep Information Retrieval}, AUTHOR = {MacAvaney, Sean and Hui, Kai and Yates, Andrew}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1707.00189}, EPRINT = {1707.00189}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that---given a weak training set of pseudo-queries, documents, relevance information---filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article's headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection.}, }
Endnote
%0 Report %A MacAvaney, Sean %A Hui, Kai %A Yates, Andrew %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T An Approach for Weakly-Supervised Deep Information Retrieval : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-06C5-C %U http://arxiv.org/abs/1707.00189 %D 2017 %X Recent developments in neural information retrieval models have been promising, but a problem remains: human relevance judgments are expensive to produce, while neural models require a considerable amount of training data. In an attempt to fill this gap, we present an approach that---given a weak training set of pseudo-queries, documents, relevance information---filters the data to produce effective positive and negative query-document pairs. This allows large corpora to be used as neural IR model training data, while eliminating training examples that do not transfer well to relevance scoring. The filters include unsupervised ranking heuristics and a novel measure of interaction similarity. We evaluate our approach using a news corpus with article headlines acting as pseudo-queries and article content as documents, with implicit relevance between an article's headline and its content. By using our approach to train state-of-the-art neural IR models and comparing to established baselines, we find that training data generated by our approach can lead to good results on a benchmark test collection. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[55]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Approximate Functional Dependencies,” in KDD’17, 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 2017.
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@inproceedings{MandrosKDD2017, TITLE = {Discovering Reliable Approximate Functional Dependencies}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-4503-4887-4}, DOI = {10.1145/3097983.3098062}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {KDD'17, 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, PAGES = {355--363}, ADDRESS = {Halifax, NS, Canada}, }
Endnote
%0 Conference Proceedings %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Reliable Approximate Functional Dependencies : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-065F-5 %R 10.1145/3097983.3098062 %D 2017 %B 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %Z date of event: 2017-08-13 - 2017-08-17 %C Halifax, NS, Canada %B KDD'17 %P 355 - 363 %I ACM %@ 978-1-4503-4887-4
[56]
P. Mandros, M. Boley, and J. Vreeken, “Discovering Reliable Approximate Functional Dependencies,” 2017. [Online]. Available: http://arxiv.org/abs/1705.09391. (arXiv: 1705.09391)
Abstract
Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or $\alpha$-approximate top-$k$ dependencies? These are exactly the questions we answer in this paper. As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity.
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@online{DBLP:journals/corr/MandrosBV17, TITLE = {Discovering Reliable Approximate Functional Dependencies}, AUTHOR = {Mandros, Panagiotis and Boley, Mario and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1705.09391}, EPRINT = {1705.09391}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or $\alpha$-approximate top-$k$ dependencies? These are exactly the questions we answer in this paper. As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity.}, }
Endnote
%0 Report %A Mandros, Panagiotis %A Boley, Mario %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Reliable Approximate Functional Dependencies : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90F8-D %U http://arxiv.org/abs/1705.09391 %D 2017 %X Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other attributes in the data? How can we reliably, without bias to sample size or dimensionality, measure the strength of such a dependence? And, how can we efficiently discover the optimal or $\alpha$-approximate top-$k$ dependencies? These are exactly the questions we answer in this paper. As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed. Moreover, we give an effective optimistic estimator of this score, by which for the first time we can mine the approximate functional dependencies from data with guarantees of optimality. Empirical evaluation shows that the derived score achieves a good bias for variance trade-off, can be used within an efficient discovery algorithm, and indeed discovers meaningful dependencies. Most important, it remains reliable in the face of data sparsity. %K Computer Science, Databases, cs.DB,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Information Theory, cs.IT,Mathematics, Information Theory, math.IT
[57]
A. Marx and J. Vreeken, “Telling Cause from Effect Using MDL-Based Local and Global Regression,” in 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, LA, USA, 2017.
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@inproceedings{MarxICDM2017, TITLE = {Telling Cause from Effect Using {MDL}-Based Local and Global Regression}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-5386-3835-4}, DOI = {10.1109/ICDM.2017.40}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining (ICDM 2017)}, PAGES = {307--316}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Telling Cause from Effect Using MDL-Based Local and Global Regression : %G eng %U http://hdl.handle.net/21.11116/0000-0000-63C4-3 %R 10.1109/ICDM.2017.40 %D 2017 %B 17th IEEE International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining %P 307 - 316 %I IEEE %@ 978-1-5386-3835-4
[58]
A. Marx and J. Vreeken, “Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length,” 2017. [Online]. Available: http://arxiv.org/abs/1702.06385. (arXiv: 1702.06385)
Abstract
Given data over the joint distribution of two univariate or multivariate random variables $X$ and $Y$ of mixed or single type data, we consider the problem of inferring the most likely causal direction between $X$ and $Y$. We take an information theoretic approach, from which it follows that first describing the data over cause and then that of effect given cause is shorter than the reverse direction. For practical inference, we propose a score for causal models for mixed type data based on the Minimum Description Length (MDL) principle. In particular, we model dependencies between $X$ and $Y$ using classification and regression trees. Inferring the optimal model is NP-hard, and hence we propose Crack, a fast greedy algorithm to infer the most likely causal direction directly from the data. Empirical evaluation on synthetic, benchmark, and real world data shows that Crack reliably and with high accuracy infers the correct causal direction on both univariate and multivariate cause--effect pairs over both single and mixed type data.
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@online{DBLP:journals/corr/MarxV17, TITLE = {Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length}, AUTHOR = {Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1702.06385}, EPRINT = {1702.06385}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Given data over the joint distribution of two univariate or multivariate random variables $X$ and $Y$ of mixed or single type data, we consider the problem of inferring the most likely causal direction between $X$ and $Y$. We take an information theoretic approach, from which it follows that first describing the data over cause and then that of effect given cause is shorter than the reverse direction. For practical inference, we propose a score for causal models for mixed type data based on the Minimum Description Length (MDL) principle. In particular, we model dependencies between $X$ and $Y$ using classification and regression trees. Inferring the optimal model is NP-hard, and hence we propose Crack, a fast greedy algorithm to infer the most likely causal direction directly from the data. Empirical evaluation on synthetic, benchmark, and real world data shows that Crack reliably and with high accuracy infers the correct causal direction on both univariate and multivariate cause--effect pairs over both single and mixed type data.}, }
Endnote
%0 Report %A Marx, Alexander %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference on Multivariate Mixed-Type Data by Minimum Description Length : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90EF-3 %U http://arxiv.org/abs/1702.06385 %D 2017 %X Given data over the joint distribution of two univariate or multivariate random variables $X$ and $Y$ of mixed or single type data, we consider the problem of inferring the most likely causal direction between $X$ and $Y$. We take an information theoretic approach, from which it follows that first describing the data over cause and then that of effect given cause is shorter than the reverse direction. For practical inference, we propose a score for causal models for mixed type data based on the Minimum Description Length (MDL) principle. In particular, we model dependencies between $X$ and $Y$ using classification and regression trees. Inferring the optimal model is NP-hard, and hence we propose Crack, a fast greedy algorithm to infer the most likely causal direction directly from the data. Empirical evaluation on synthetic, benchmark, and real world data shows that Crack reliably and with high accuracy infers the correct causal direction on both univariate and multivariate cause--effect pairs over both single and mixed type data. %K Statistics, Machine Learning, stat.ML,Computer Science, Learning, cs.LG
[59]
F. Meawad, M. H. Gad-Elrab, and E. Hemayed, “Designing Mobile Augmented Reality Experiences Using Friendly Markers,” in 4th International Conference on User Science and Engineering (i-USEr 2016), Melaka, Malaysia, 2017.
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@inproceedings{Meawad2017, TITLE = {Designing Mobile Augmented Reality Experiences Using Friendly Markers}, AUTHOR = {Meawad, Fatma and Gad-Elrab, Mohamed H. and Hemayed, Elsayed}, LANGUAGE = {eng}, ISBN = {978-1-5090-263-9}, DOI = {10.1109/IUSER.2016.7857937}, PUBLISHER = {IEEE}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {4th International Conference on User Science and Engineering (i-USEr 2016)}, PAGES = {75--80}, ADDRESS = {Melaka, Malaysia}, }
Endnote
%0 Conference Proceedings %A Meawad, Fatma %A Gad-Elrab, Mohamed H. %A Hemayed, Elsayed %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Designing Mobile Augmented Reality Experiences Using Friendly Markers : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-CF28-A %R 10.1109/IUSER.2016.7857937 %D 2017 %B 4th International Conference on User Science and Engineering %Z date of event: 2016-08-23 - 2016-08-25 %C Melaka, Malaysia %B 4th International Conference on User Science and Engineering %P 75 - 80 %I IEEE %@ 978-1-5090-263-9
[60]
S. Metzger, R. Schenkel, and M. Sydow, “QBEES: Query-by-Example Entity Search in Semantic Knowledge Graphs Based on Maximal Aspects, Diversity-awareness and Relaxation,” Journal of Intelligent Information Systems, vol. 49, no. 3, 2017.
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@article{Metzger2017, TITLE = {{QBEES}: Query-by-Example Entity Search in Semantic Knowledge Graphs Based on Maximal Aspects, Diversity-awareness and Relaxation}, AUTHOR = {Metzger, Steffen and Schenkel, Ralf and Sydow, Marcin}, LANGUAGE = {eng}, DOI = {10.1007/s10844-017-0443-x}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {Journal of Intelligent Information Systems}, VOLUME = {49}, NUMBER = {3}, PAGES = {333--366}, }
Endnote
%0 Journal Article %A Metzger, Steffen %A Schenkel, Ralf %A Sydow, Marcin %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T QBEES: Query-by-Example Entity Search in Semantic Knowledge Graphs Based on Maximal Aspects, Diversity-awareness and Relaxation : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-557B-8 %R 10.1007/s10844-017-0443-x %7 2017 %D 2017 %J Journal of Intelligent Information Systems %V 49 %N 3 %& 333 %P 333 - 366
[61]
S. Metzler, S. Günnemann, and P. Miettinen, “Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques,” in 16th IEEE International Conference on Data Mining (ICDM 2016), Barcelona, Spain, 2017.
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@inproceedings{metzler16hyperbolae, TITLE = {Hyperbolae Are No Hyperbole: {Modelling} Communities That Are Not Cliques}, AUTHOR = {Metzler, Saskia and G{\"u}nnemann, Stephan and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-5090-5473-2}, DOI = {10.1109/ICDM.2016.0044}, PUBLISHER = {IEEE}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {16th IEEE International Conference on Data Mining (ICDM 2016)}, PAGES = {330--339}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Metzler, Saskia %A Günnemann, Stephan %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-225F-F %R 10.1109/ICDM.2016.0044 %D 2017 %8 02.02.2017 %B 16th International Conference on Data Mining %Z date of event: 2016-12-12 - 2016-12-15 %C Barcelona, Spain %B 16th IEEE International Conference on Data Mining %P 330 - 339 %I IEEE %@ 978-1-5090-5473-2
[62]
P. Mirza, S. Razniewski, F. Darari, and G. Weikum, “Cardinal Virtues: Extracting Relation Cardinalities from Text,” in The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, Canada, 2017.
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@inproceedings{MirzaACL2017, TITLE = {Cardinal Virtues: {E}xtracting Relation Cardinalities from Text}, AUTHOR = {Mirza, Paramita and Razniewski, Simon and Darari, Fariz and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-945626-76-0}, DOI = {10.18653/v1/P17-2055}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017)}, PAGES = {347--351}, ADDRESS = {Vancouver, Canada}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Razniewski, Simon %A Darari, Fariz %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Cardinal Virtues: Extracting Relation Cardinalities from Text : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-F9F8-7 %R 10.18653/v1/P17-2055 %D 2017 %B The 55th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2017-07-30 - 2017-08-04 %C Vancouver, Canada %B The 55th Annual Meeting of the Association for Computational Linguistics %P 347 - 351 %I ACL %@ 978-1-945626-76-0 %U http://aclweb.org/anthology/P17-2055
[63]
P. Mirza, S. Razniewski, F. Darari, and G. Weikum, “Cardinal Virtues: Extracting Relation Cardinalities from Text,” 2017. [Online]. Available: http://arxiv.org/abs/1704.04455. (arXiv: 1704.04455)
Abstract
Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.
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@online{Mirza2017, TITLE = {Cardinal Virtues: Extracting Relation Cardinalities from Text}, AUTHOR = {Mirza, Paramita and Razniewski, Simon and Darari, Fariz and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1704.04455}, EPRINT = {1704.04455}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.}, }
Endnote
%0 Report %A Mirza, Paramita %A Razniewski, Simon %A Darari, Fariz %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Cardinal Virtues: Extracting Relation Cardinalities from Text : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-8128-9 %U http://arxiv.org/abs/1704.04455 %D 2017 %X Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations. %K Computer Science, Computation and Language, cs.CL
[64]
A. Mishra and K. Berberich, “How do Order and Proximity Impact the Readability of Event Summaries?,” in Advances in Information Retrieval (ECIR 2017), Aberdeen, UK, 2017.
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@inproceedings{DBLP:conf/ecir/MishraB17, TITLE = {How do Order and Proximity Impact the Readability of Event Summaries?}, AUTHOR = {Mishra, Arunav and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-56607-8}, DOI = {10.1007/978-3-319-56608-5_17}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2017)}, EDITOR = {Jose, Joemon M. and Hauff, Claudia and Altingovde, Ismail Sengor and Song, Dawei and Albakour, Dyaa and Watt, Stuart and Tait, John}, PAGES = {212--225}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10193}, ADDRESS = {Aberdeen, UK}, }
Endnote
%0 Conference Proceedings %A Mishra, Arunav %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T How do Order and Proximity Impact the Readability of Event Summaries? : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-20D9-B %R 10.1007/978-3-319-56608-5_17 %D 2017 %B 39th European Conference on Information Retrieval %Z date of event: 2017-04-09 - 2017-04-13 %C Aberdeen, UK %B Advances in Information Retrieval %E Jose, Joemon M.; Hauff, Claudia; Altingovde, Ismail Sengor; Song, Dawei; Albakour, Dyaa; Watt, Stuart; Tait, John %P 212 - 225 %I Springer %@ 978-3-319-56607-8 %B Lecture Notes in Computer Science %N 10193
[65]
S. Mukherjee, “Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.
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@phdthesis{Mukherjeephd17, TITLE = {Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities}, AUTHOR = {Mukherjee, Subhabrata}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-69269}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.}, }
Endnote
%0 Thesis %A Mukherjee, Subhabrata %Y Weikum, Gerhard %A referee: Han, Jiawei %A referee: Günnemann, Stephan %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-A648-0 %U urn:nbn:de:bsz:291-scidok-69269 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P 166 p. %V phd %9 phd %X One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information. %U http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=dehttp://scidok.sulb.uni-saarland.de/volltexte/2017/6926/
[66]
S. Mukherjee and G. Weikum, “People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities,” 2017. [Online]. Available: http://arxiv.org/abs/1705.02667. (arXiv: 1705.02667)
Abstract
Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.
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@online{Mukerjee_arXiv1705.02667, TITLE = {People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities}, AUTHOR = {Mukherjee, Subhabrata and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1705.02667}, EPRINT = {1705.02667}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.}, }
Endnote
%0 Report %A Mukherjee, Subhabrata %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-80F7-0 %U http://arxiv.org/abs/1705.02667 %D 2017 %X Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR,cs.SI,Statistics, Machine Learning, stat.ML
[67]
S. Mukherjee, G. Weikum, and C. Danescu-Niculescu-Mizil, “People on Drugs: Credibility of User Statements in Health Communities,” 2017. [Online]. Available: http://arxiv.org/abs/1705.02522. (arXiv: 1705.02522)
Abstract
Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.
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@online{Mukherjee_arXiv2017, TITLE = {People on Drugs: Credibility of User Statements in Health Communities}, AUTHOR = {Mukherjee, Subhabrata and Weikum, Gerhard and Danescu-Niculescu-Mizil, Cristian}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1705.02522}, EPRINT = {1705.02522}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.}, }
Endnote
%0 Report %A Mukherjee, Subhabrata %A Weikum, Gerhard %A Danescu-Niculescu-Mizil, Cristian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T People on Drugs: Credibility of User Statements in Health Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-80FE-2 %U http://arxiv.org/abs/1705.02522 %D 2017 %X Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR,cs.SI,Statistics, Machine Learning, stat.ML
[68]
S. Mukherjee, H. Lamba, and G. Weikum, “Item Recommendation with Evolving User Preferences and Experience,” 2017. [Online]. Available: http://arxiv.org/abs/1705.02519. (arXiv: 1705.02519)
Abstract
Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in the user's writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the user's maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a user's interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels.
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@online{Mukherjee2017d, TITLE = {Item Recommendation with Evolving User Preferences and Experience}, AUTHOR = {Mukherjee, Subhabrata and Lamba, Hemank and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1705.02519}, DOI = {10.1109/ICDM.2015.111}, EPRINT = {1705.02519}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in the user's writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the user's maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a user's interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels.}, }
Endnote
%0 Report %A Mukherjee, Subhabrata %A Lamba, Hemank %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Item Recommendation with Evolving User Preferences and Experience : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-8103-C %R 10.1109/ICDM.2015.111 %U http://arxiv.org/abs/1705.02519 %D 2017 %X Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in the user's writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the user's maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a user's interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR,cs.SI,Statistics, Machine Learning, stat.ML
[69]
S. Mukherjee, S. Guennemann, and G. Weikum, “Personalized Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion,” 2017. [Online]. Available: http://arxiv.org/abs/1705.02669. (arXiv: 1705.02669)
Abstract
Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time. This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also outperforms state-of-the-art methods for predicting item ratings.
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@online{Mukherjee2017, TITLE = {Personalized Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion}, AUTHOR = {Mukherjee, Subhabrata and Guennemann, Stephan and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1705.02669}, DOI = {10.1145/2939672.2939780}, EPRINT = {1705.02669}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time. This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also outperforms state-of-the-art methods for predicting item ratings.}, }
Endnote
%0 Report %A Mukherjee, Subhabrata %A Guennemann, Stephan %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Personalized Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-80BE-3 %R 10.1145/2939672.2939780 %U http://arxiv.org/abs/1705.02669 %D 2017 %X Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time. This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also outperforms state-of-the-art methods for predicting item ratings. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR,cs.SI,Statistics, Machine Learning, stat.ML
[70]
S. Mukherjee, K. Popat, and G. Weikum, “Exploring Latent Semantic Factors to Find Useful Product Reviews,” in Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017), Houston, TX, USA, 2017.
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@inproceedings{MukherjeeSDM2017, TITLE = {Exploring Latent Semantic Factors to Find Useful Product Reviews}, AUTHOR = {Mukherjee, Subhabrata and Popat, Kashyap and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-61197-497-3}, DOI = {10.1137/1.9781611974973.54}, PUBLISHER = {SIAM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017)}, PAGES = {480--488}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Mukherjee, Subhabrata %A Popat, Kashyap %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exploring Latent Semantic Factors to Find Useful Product Reviews : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4CD4-6 %R 10.1137/1.9781611974973.54 %D 2017 %B 17th SIAM International Conference on Data Mining %Z date of event: 2017-04-27 - 2017-04-29 %C Houston, TX, USA %B Proceedings of the Seventeenth SIAM International Conference on Data Mining %P 480 - 488 %I SIAM %@ 978-1-61197-497-3
[71]
S. Mukherjee, K. Popat, and G. Weikum, “Exploring Latent Semantic Factors to Find Useful Product Reviews,” 2017. [Online]. Available: http://arxiv.org/abs/1705.02518. (arXiv: 1705.02518)
Abstract
Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews.
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@online{Mukjherjee2017e, TITLE = {Exploring Latent Semantic Factors to Find Useful Product Reviews}, AUTHOR = {Mukherjee, Subhabrata and Popat, Kashyap and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1705.02518}, EPRINT = {1705.02518}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews.}, }
Endnote
%0 Report %A Mukherjee, Subhabrata %A Popat, Kashyap %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exploring Latent Semantic Factors to Find Useful Product Reviews : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-811C-5 %U http://arxiv.org/abs/1705.02518 %D 2017 %X Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR,cs.SI,Statistics, Machine Learning, stat.ML
[72]
S. Mukherjee, S. Dutta, and G. Weikum, “Credible Review Detection with Limited Information using Consistency Analysis,” 2017. [Online]. Available: http://arxiv.org/abs/1705.02668. (arXiv: 1705.02668)
Abstract
Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased -- entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users -- which might not be readily available in several domains -- that provide only limited interpretability as to why a review is deemed non-credible. This paper presents a novel approach to address the above issues. We utilize latent topic models leveraging review texts, item ratings, and timestamps to derive consistency features without relying on item/user histories, unavailable for "long-tail" items/users. We develop models, for computing review credibility scores to provide interpretable evidence for non-credible reviews, that are also transferable to other domains -- addressing the scarcity of labeled data. Experiments on real-world datasets demonstrate improvements over state-of-the-art baselines.
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@online{Mukherjee2017b, TITLE = {Credible Review Detection with Limited Information using Consistency Analysis}, AUTHOR = {Mukherjee, Subhabrata and Dutta, Sourav and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1705.02668}, EPRINT = {1705.02668}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased -- entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users -- which might not be readily available in several domains -- that provide only limited interpretability as to why a review is deemed non-credible. This paper presents a novel approach to address the above issues. We utilize latent topic models leveraging review texts, item ratings, and timestamps to derive consistency features without relying on item/user histories, unavailable for "long-tail" items/users. We develop models, for computing review credibility scores to provide interpretable evidence for non-credible reviews, that are also transferable to other domains -- addressing the scarcity of labeled data. Experiments on real-world datasets demonstrate improvements over state-of-the-art baselines.}, }
Endnote
%0 Report %A Mukherjee, Subhabrata %A Dutta, Sourav %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Credible Review Detection with Limited Information using Consistency Analysis : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-80C1-A %U http://arxiv.org/abs/1705.02668 %D 2017 %X Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased -- entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users -- which might not be readily available in several domains -- that provide only limited interpretability as to why a review is deemed non-credible. This paper presents a novel approach to address the above issues. We utilize latent topic models leveraging review texts, item ratings, and timestamps to derive consistency features without relying on item/user histories, unavailable for "long-tail" items/users. We develop models, for computing review credibility scores to provide interpretable evidence for non-credible reviews, that are also transferable to other domains -- addressing the scarcity of labeled data. Experiments on real-world datasets demonstrate improvements over state-of-the-art baselines. %K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Information Retrieval, cs.IR,cs.SI,Statistics, Machine Learning, stat.ML
[73]
S. Neumann, R. Gemulla, and P. Miettinen, “What You Will Gain By Rounding: Theory and Algorithms for Rounding Rank,” in 16th IEEE International Conference on Data Mining (ICDM 2016), Barcelona, Spain, 2017.
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@inproceedings{neumann16what, TITLE = {What You Will Gain By Rounding: {Theory} and Algorithms for Rounding Rank}, AUTHOR = {Neumann, Stefan and Gemulla, Rainer and Miettinen, Pauli}, LANGUAGE = {eng}, DOI = {10.1109/ICDM.2016.147}, PUBLISHER = {IEEE}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {16th IEEE International Conference on Data Mining (ICDM 2016)}, EDITOR = {Bonchi, Francesco and Domingo-Ferrer, Josep and Baeza-Yates, Ricardo and Zhou, Zhi-Hua and Wu, Xindong}, PAGES = {380--389}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Neumann, Stefan %A Gemulla, Rainer %A Miettinen, Pauli %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T What You Will Gain By Rounding: Theory and Algorithms for Rounding Rank : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2265-0 %R 10.1109/ICDM.2016.147 %D 2017 %8 02.02.2017 %B 16th International Conference on Data Mining %Z date of event: 2016-12-12 - 2016-12-15 %C Barcelona, Spain %B 16th IEEE International Conference on Data Mining %E Bonchi, Francesco; Domingo-Ferrer, Josep; Baeza-Yates, Ricardo; Zhou, Zhi-Hua; Wu, Xindong %P 380 - 389 %I IEEE
[74]
S. Neumann and P. Miettinen, “Reductions for Frequency-Based Data Mining Problems,” in 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, LA, USA, 2017.
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@inproceedings{neumann17reductions, TITLE = {Reductions for Frequency-Based Data Mining Problems}, AUTHOR = {Neumann, Stefan and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-5386-3835-4}, DOI = {10.1109/ICDM.2017.128}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {17th IEEE International Conference on Data Mining (ICDM 2017)}, PAGES = {997--1002}, ADDRESS = {New Orleans, LA, USA}, }
Endnote
%0 Conference Proceedings %A Neumann, Stefan %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Reductions for Frequency-Based Data Mining Problems : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-90CE-F %R 10.1109/ICDM.2017.128 %D 2017 %B 17th IEEE International Conference on Data Mining %Z date of event: 2017-11-18 - 2017-11-21 %C New Orleans, LA, USA %B 17th IEEE International Conference on Data Mining %P 997 - 1002 %I IEEE %@ 978-1-5386-3835-4
[75]
S. Neumann and P. Miettinen, “Reductions for Frequency-Based Data Mining Problems,” 2017. [Online]. Available: http://arxiv.org/abs/1709.00900. (arXiv: 1709.00900)
Abstract
Studying the computational complexity of problems is one of the - if not the - fundamental questions in computer science. Yet, surprisingly little is known about the computational complexity of many central problems in data mining. In this paper we study frequency-based problems and propose a new type of reduction that allows us to compare the complexities of the maximal frequent pattern mining problems in different domains (e.g. graphs or sequences). Our results extend those of Kimelfeld and Kolaitis [ACM TODS, 2014] to a broader range of data mining problems. Our results show that, by allowing constraints in the pattern space, the complexities of many maximal frequent pattern mining problems collapse. These problems include maximal frequent subgraphs in labelled graphs, maximal frequent itemsets, and maximal frequent subsequences with no repetitions. In addition to theoretical interest, our results might yield more efficient algorithms for the studied problems.
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@online{Neumann_arXiv2017, TITLE = {Reductions for Frequency-Based Data Mining Problems}, AUTHOR = {Neumann, Stefan and Miettinen, Pauli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1709.00900}, EPRINT = {1709.00900}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Studying the computational complexity of problems is one of the -- if not the - fundamental questions in computer science. Yet, surprisingly little is known about the computational complexity of many central problems in data mining. In this paper we study frequency-based problems and propose a new type of reduction that allows us to compare the complexities of the maximal frequent pattern mining problems in different domains (e.g. graphs or sequences). Our results extend those of Kimelfeld and Kolaitis [ACM TODS, 2014] to a broader range of data mining problems. Our results show that, by allowing constraints in the pattern space, the complexities of many maximal frequent pattern mining problems collapse. These problems include maximal frequent subgraphs in labelled graphs, maximal frequent itemsets, and maximal frequent subsequences with no repetitions. In addition to theoretical interest, our results might yield more efficient algorithms for the studied problems.}, }
Endnote
%0 Report %A Neumann, Stefan %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Reductions for Frequency-Based Data Mining Problems : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-0654-C %U http://arxiv.org/abs/1709.00900 %D 2017 %X Studying the computational complexity of problems is one of the - if not the - fundamental questions in computer science. Yet, surprisingly little is known about the computational complexity of many central problems in data mining. In this paper we study frequency-based problems and propose a new type of reduction that allows us to compare the complexities of the maximal frequent pattern mining problems in different domains (e.g. graphs or sequences). Our results extend those of Kimelfeld and Kolaitis [ACM TODS, 2014] to a broader range of data mining problems. Our results show that, by allowing constraints in the pattern space, the complexities of many maximal frequent pattern mining problems collapse. These problems include maximal frequent subgraphs in labelled graphs, maximal frequent itemsets, and maximal frequent subsequences with no repetitions. In addition to theoretical interest, our results might yield more efficient algorithms for the studied problems. %K Computer Science, Computational Complexity, cs.CC
[76]
D. B. Nguyen, “Joint Models for Information and Knowledge Extraction,” Universität des Saarlandes, Saarbrücken, 2017.
Abstract
Information and knowledge extraction from natural language text is a key asset for question answering, semantic search, automatic summarization, and other machine reading applications. There are many sub-tasks involved such as named entity recognition, named entity disambiguation, co-reference resolution, relation extraction, event detection, discourse parsing, and others. Solving these tasks is challenging as natural language text is unstructured, noisy, and ambiguous. Key challenges, which focus on identifying and linking named entities, as well as discovering relations between them, include: • High NERD Quality. Named entity recognition and disambiguation, NERD for short, are preformed first in the extraction pipeline. Their results may affect other downstream tasks. • Coverage vs. Quality of Relation Extraction. Model-based information extraction methods achieve high extraction quality at low coverage, whereas open information extraction methods capture relational phrases between entities. However, the latter degrades in quality by non-canonicalized and noisy output. These limitations need to be overcome. • On-the-fly Knowledge Acquisition. Real-world applications such as question answering, monitoring content streams, etc. demand on-the-fly knowledge acquisition. Building such an end-to-end system is challenging because it requires high throughput, high extraction quality, and high coverage. This dissertation addresses the above challenges, developing new methods to advance the state of the art. The first contribution is a robust model for joint inference between entity recognition and disambiguation. The second contribution is a novel model for relation extraction and entity disambiguation on Wikipediastyle text. The third contribution is an end-to-end system for constructing querydriven, on-the-fly knowledge bases.
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@phdthesis{Nguyenphd2017, TITLE = {Joint Models for Information and Knowledge Extraction}, AUTHOR = {Nguyen, Dat Ba}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-ds-269433}, DOI = {10.22028/D291-26943}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Information and knowledge extraction from natural language text is a key asset for question answering, semantic search, automatic summarization, and other machine reading applications. There are many sub-tasks involved such as named entity recognition, named entity disambiguation, co-reference resolution, relation extraction, event detection, discourse parsing, and others. Solving these tasks is challenging as natural language text is unstructured, noisy, and ambiguous. Key challenges, which focus on identifying and linking named entities, as well as discovering relations between them, include: \mbox{$\bullet$} High NERD Quality. Named entity recognition and disambiguation, NERD for short, are preformed first in the extraction pipeline. Their results may affect other downstream tasks. \mbox{$\bullet$} Coverage vs. Quality of Relation Extraction. Model-based information extraction methods achieve high extraction quality at low coverage, whereas open information extraction methods capture relational phrases between entities. However, the latter degrades in quality by non-canonicalized and noisy output. These limitations need to be overcome. \mbox{$\bullet$} On-the-fly Knowledge Acquisition. Real-world applications such as question answering, monitoring content streams, etc. demand on-the-fly knowledge acquisition. Building such an end-to-end system is challenging because it requires high throughput, high extraction quality, and high coverage. This dissertation addresses the above challenges, developing new methods to advance the state of the art. The first contribution is a robust model for joint inference between entity recognition and disambiguation. The second contribution is a novel model for relation extraction and entity disambiguation on Wikipediastyle text. The third contribution is an end-to-end system for constructing querydriven, on-the-fly knowledge bases.}, }
Endnote
%0 Thesis %A Nguyen, Dat Ba %Y Weikum, Gerhard %A referee: Theobald, Martin %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Joint Models for Information and Knowledge Extraction : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-890F-9 %U urn:nbn:de:bsz:291-scidok-ds-269433 %R 10.22028/D291-26943 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P 89 p. %V phd %9 phd %X Information and knowledge extraction from natural language text is a key asset for question answering, semantic search, automatic summarization, and other machine reading applications. There are many sub-tasks involved such as named entity recognition, named entity disambiguation, co-reference resolution, relation extraction, event detection, discourse parsing, and others. Solving these tasks is challenging as natural language text is unstructured, noisy, and ambiguous. Key challenges, which focus on identifying and linking named entities, as well as discovering relations between them, include: • High NERD Quality. Named entity recognition and disambiguation, NERD for short, are preformed first in the extraction pipeline. Their results may affect other downstream tasks. • Coverage vs. Quality of Relation Extraction. Model-based information extraction methods achieve high extraction quality at low coverage, whereas open information extraction methods capture relational phrases between entities. However, the latter degrades in quality by non-canonicalized and noisy output. These limitations need to be overcome. • On-the-fly Knowledge Acquisition. Real-world applications such as question answering, monitoring content streams, etc. demand on-the-fly knowledge acquisition. Building such an end-to-end system is challenging because it requires high throughput, high extraction quality, and high coverage. This dissertation addresses the above challenges, developing new methods to advance the state of the art. The first contribution is a robust model for joint inference between entity recognition and disambiguation. The second contribution is a novel model for relation extraction and entity disambiguation on Wikipediastyle text. The third contribution is an end-to-end system for constructing querydriven, on-the-fly knowledge bases. %U https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/26895
[77]
D. B. Nguyen, M. Theobald, and G. Weikum, “J-REED: Joint Relation Extraction and Entity Disambiguation,” in CIKM’17, 26th ACM International Conference on Information and Knowledge Management, Singapore, Singapore, 2017.
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@inproceedings{Nguyen_CIKM2017, TITLE = {J-{REED}: {Joint Relation Extraction and Entity Disambiguation}}, AUTHOR = {Nguyen, Dat Ba and Theobald, Martin and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4918-5}, DOI = {10.1145/3132847.3133090}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {CIKM'17, 26th ACM International Conference on Information and Knowledge Management}, PAGES = {2227--2230}, ADDRESS = {Singapore, Singapore}, }
Endnote
%0 Conference Proceedings %A Nguyen, Dat Ba %A Theobald, Martin %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T J-REED: Joint Relation Extraction and Entity Disambiguation : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3B9D-E %R 10.1145/3132847.3133090 %D 2017 %B 26th ACM International Conference on Information and Knowledge Management %Z date of event: 2017-11-06 - 2017-11-10 %C Singapore, Singapore %B CIKM'17 %P 2227 - 2230 %I ACM %@ 978-1-4503-4918-5
[78]
A. Nikitin, C. Laoudias, G. Chatzimilioudis, P. Karras, and D. Zeinalipour-Yazti, “Indoor Localization Accuracy Estimation from Fingerprint Data,” in 18th IEEE International Conference on Mobile Data Management (MDM 2017), Daejeon, South Korea, 2017.
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@inproceedings{mdm17-spate, TITLE = {Indoor Localization Accuracy Estimation from Fingerprint Data}, AUTHOR = {Nikitin, Artyom and Laoudias, Christos and Chatzimilioudis, Georgios and Karras, Panagiotis and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-1-5386-3932-0}, DOI = {10.1109/MDM.2017.34}, PUBLISHER = {IEEE}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {18th IEEE International Conference on Mobile Data Management (MDM 2017)}, PAGES = {196--205}, ADDRESS = {Daejeon, South Korea}, }
Endnote
%0 Conference Proceedings %A Nikitin, Artyom %A Laoudias, Christos %A Chatzimilioudis, Georgios %A Karras, Panagiotis %A Zeinalipour-Yazti, Demetrios %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Indoor Localization Accuracy Estimation from Fingerprint Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-0832-6 %R 10.1109/MDM.2017.34 %D 2017 %B 18th IEEE International Conference on Mobile Data Management %Z date of event: 2017-05-29 - 2017-06-01 %C Daejeon, South Korea %B 18th IEEE International Conference on Mobile Data Management %P 196 - 205 %I IEEE %@ 978-1-5386-3932-0
[79]
A. Nikitin, C. Laoudias, G. Chatzimilioudis, P. Karras, and D. Zeinalipour-Yazti, “ACCES: Offline Accuracy Estimation for Fingerprint-based Localization,” in 18th IEEE International Conference on Mobile Data Management (MDM 2017), Daejeon, South Korea, 2017.
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@inproceedings{mdm17-spate-demo, TITLE = {{ACCES}: Offline Accuracy Estimation for Fingerprint-based Localization}, AUTHOR = {Nikitin, Artyom and Laoudias, Christos and Chatzimilioudis, Georgios and Karras, Panagiotis and Zeinalipour-Yazti, Demetrios}, LANGUAGE = {eng}, ISBN = {978-1-5386-3932-0}, DOI = {10.1109/MDM.2017.61}, PUBLISHER = {IEEE Computer Society}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {18th IEEE International Conference on Mobile Data Management (MDM 2017)}, PAGES = {358--359}, ADDRESS = {Daejeon, South Korea}, }
Endnote
%0 Conference Proceedings %A Nikitin, Artyom %A Laoudias, Christos %A Chatzimilioudis, Georgios %A Karras, Panagiotis %A Zeinalipour-Yazti, Demetrios %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T ACCES: Offline Accuracy Estimation for Fingerprint-based Localization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-082D-3 %R 10.1109/MDM.2017.61 %D 2017 %B 18th IEEE International Conference on Mobile Data Management %Z date of event: 2017-05-29 - 2017-06-01 %C Daejeon, South Korea %B 18th IEEE International Conference on Mobile Data Management %P 358 - 359 %I IEEE Computer Society %@ 978-1-5386-3932-0
[80]
S. Paramonov, D. Stepanova, and P. Miettinen, “Hybrid ASP-based Approach to Pattern Mining,” in Lecture Notes in Computer Science, London, UK, 2017, vol. 10364.
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@inproceedings{StepanovaRR2017, TITLE = {Hybrid {ASP}-based Approach to Pattern Mining}, AUTHOR = {Paramonov, Sergey and Stepanova, Daria and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-3-319-61251-5}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Rules and Reasoning (RuleML+RR 2017)}, PAGES = {199--214}, BOOKTITLE = {Lecture Notes in Computer Science}, VOLUME = {10364}, ADDRESS = {London, UK}, }
Endnote
%0 Conference Proceedings %A Paramonov, Sergey %A Stepanova, Daria %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Hybrid ASP-based Approach to Pattern Mining : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-8450-8 %D 2017 %B International Joint Conference on Rules and Reasoning %Z date of event: 2017-07-12 - 2017-07-15 %C London, UK %B Rules and Reasoning %P 199 - 214 %I Springer %@ 978-3-319-61251-5 %B Lecture Notes in Computer Science %V 10364
[81]
T. Pelilissier Tanon, D. Stepanova, S. Razniewski, P. Mirza, and G. Weikum, “Completeness-Aware Rule Learning from Knowledge Graphs,” in The Semantic Web -- ISWC 2017, Vienna, Austria, 2017.
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@inproceedings{StepanovaISWC2017, TITLE = {Completeness-Aware Rule Learning from Knowledge Graphs}, AUTHOR = {Pelilissier Tanon, Thomas and Stepanova, Daria and Razniewski, Simon and Mirza, Paramita and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-319-68287-7}, DOI = {10.1007/978-3-319-68288-4_30}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {The Semantic Web -- ISWC 2017}, EDITOR = {d'Amato, Claudia and Fernandez, Miriam and Tamma, Valentina and Lecue, Freddy and Cudr{\'e}-Mauroux, Philippe and Sequeda, Juan and Lange, Christoph and Hefflin, Jeff}, PAGES = {507--525}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {10587}, ADDRESS = {Vienna, Austria}, }
Endnote
%0 Conference Proceedings %A Pelilissier Tanon, Thomas %A Stepanova, Daria %A Razniewski, Simon %A Mirza, Paramita %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Completeness-Aware Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-55D9-3 %R 10.1007/978-3-319-68288-4_30 %D 2017 %B 16th International Semantic Web Conference %Z date of event: 2017-10-21 - 2017-10-25 %C Vienna, Austria %B The Semantic Web -- ISWC 2017 %E d'Amato, Claudia; Fernandez, Miriam; Tamma, Valentina; Lecue, Freddy; Cudré-Mauroux, Philippe; Sequeda, Juan; Lange, Christoph; Hefflin, Jeff %P 507 - 525 %I Springer %@ 978-3-319-68287-7 %B Lecture Notes in Computer Science %N 10587 %U https://iswc2017.ai.wu.ac.at/wp-content/uploads/papers/MainProceedings/324.pdf
[82]
R. Pienta, M. Kahng, Z. Lin, J. Vreeken, P. Talukdar, J. Abello, G. Parameswaran, and D. H. Chau, “FACETS: Adaptive Local Exploration of Large Graphs,” in Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017), Houston, TX, USA, 2017.
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@inproceedings{pienta:17:facets, TITLE = {{FACETS}: {A}daptive Local Exploration of Large Graphs}, AUTHOR = {Pienta, Robert and Kahng, Minsuk and Lin, Zhang and Vreeken, Jilles and Talukdar, Partha and Abello, James and Parameswaran, Ganesh and Chau, Duen Horng}, LANGUAGE = {eng}, ISBN = {978-1-611974-87-4}, DOI = {10.1137/1.9781611974973.67}, PUBLISHER = {SIAM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017)}, EDITOR = {Chawla, Nitesh and Wang, Wei}, PAGES = {597--605}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Pienta, Robert %A Kahng, Minsuk %A Lin, Zhang %A Vreeken, Jilles %A Talukdar, Partha %A Abello, James %A Parameswaran, Ganesh %A Chau, Duen Horng %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T FACETS: Adaptive Local Exploration of Large Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4BEA-D %R 10.1137/1.9781611974973.67 %D 2017 %B 17th SIAM International Conference on Data Mining %Z date of event: 2017-04-27 - 2017-04-29 %C Houston, TX, USA %B Proceedings of the Seventeenth SIAM International Conference on Data Mining %E Chawla, Nitesh; Wang, Wei %P 597 - 605 %I SIAM %@ 978-1-611974-87-4
[83]
E. Pitoura, P. Tsaparas, G. Flouris, I. Fundulaki, P. Papadakos, S. Abiteboul, and G. Weikum, “On Measuring Bias in Online Information,” 2017. [Online]. Available: http://arxiv.org/abs/1704.05730. (arXiv: 1704.05730)
Abstract
Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.
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@online{Pitoura2017, TITLE = {On Measuring Bias in Online Information}, AUTHOR = {Pitoura, Evaggelia and Tsaparas, Panayiotis and Flouris, Giorgos and Fundulaki, Irini and Papadakos, Panagiotis and Abiteboul, Serge and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1704.05730}, EPRINT = {1704.05730}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems.}, }
Endnote
%0 Report %A Pitoura, Evaggelia %A Tsaparas, Panayiotis %A Flouris, Giorgos %A Fundulaki, Irini %A Papadakos, Panagiotis %A Abiteboul, Serge %A Weikum, Gerhard %+ External Organizations External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T On Measuring Bias in Online Information : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-8123-4 %U http://arxiv.org/abs/1704.05730 %D 2017 %X Bias in online information has recently become a pressing issue, with search engines, social networks and recommendation services being accused of exhibiting some form of bias. In this vision paper, we make the case for a systematic approach towards measuring bias. To this end, we discuss formal measures for quantifying the various types of bias, we outline the system components necessary for realizing them, and we highlight the related research challenges and open problems. %K Computer Science, Databases, cs.DB,Computer Science, Computers and Society, cs.CY
[84]
K. Popat, “Assessing the Credibility of Claims on the Web,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{PopatWWW2017b, TITLE = {Assessing the Credibility of Claims on the {Web}}, AUTHOR = {Popat, Kashyap}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3053379}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {735--739}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Popat, Kashyap %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Assessing the Credibility of Claims on the Web : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-90CC-2 %R 10.1145/3041021.3053379 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 735 - 739 %I ACM %@ 978-1-4503-4914-7
[85]
K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum, “Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{PopatWWW2017, TITLE = {Where the Truth Lies: {E}xplaining the Credibility of Emerging Claims on the {W}eb and Social Media}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3055133}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {1003--1012}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Popat, Kashyap %A Mukherjee, Subhabrata %A Strötgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4CD8-D %R 10.1145/3041021.3055133 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 1003 - 1012 %I ACM %@ 978-1-4503-4914-7
[86]
S. Razniewski, V. Balaraman, and W. Nutt, “Doctoral Advisor or Medical Condition: Towards Entity-Specific Rankings of Knowledge Base Properties,” in Advanced Data Mining and Applications (ADMA 2017), Singapore, 2017.
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@inproceedings{Razniewski_ADMA2017, TITLE = {Doctoral Advisor or Medical Condition: {T}owards Entity-Specific Rankings of Knowledge Base Properties}, AUTHOR = {Razniewski, Simon and Balaraman, Vevake and Nutt, Werner}, LANGUAGE = {eng}, ISBN = {978-3-319-69178-7}, DOI = {10.1007/978-3-319-69179-4_37}, PUBLISHER = {Springer}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Advanced Data Mining and Applications (ADMA 2017)}, EDITOR = {Cong, Gao and Peng, Wen-Chin and Zhang, Wei Emma and Li, Chengliang and Sun, Aixin}, PAGES = {526--540}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {10604}, ADDRESS = {Singapore}, }
Endnote
%0 Conference Proceedings %A Razniewski, Simon %A Balaraman, Vevake %A Nutt, Werner %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Doctoral Advisor or Medical Condition: Towards Entity-Specific Rankings of Knowledge Base Properties : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-2C05-A %R 10.1007/978-3-319-69179-4_37 %D 2017 %B 13th International Conference on Advanced Data Mining and Applications %Z date of event: 2017-11-05 - 2017-11-06 %C Singapore %B Advanced Data Mining and Applications %E Cong, Gao; Peng, Wen-Chin; Zhang, Wei Emma; Li, Chengliang; Sun, Aixin %P 526 - 540 %I Springer %@ 978-3-319-69178-7 %B Lecture Notes in Artificial Intelligence %N 10604
[87]
B. Roel, J. Vreeken, and A. Siebes, “Efficiently Discovering Unexpected Pattern-Co-Occurrences,” in Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017), Houston, TX, USA, 2017.
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@inproceedings{RoelSDM2017, TITLE = {Efficiently Discovering Unexpected Pattern-Co-Occurrences}, AUTHOR = {Roel, Bertens and Vreeken, Jilles and Siebes, Arno}, LANGUAGE = {eng}, ISBN = {978-1-611974-87-4}, DOI = {10.1137/1.9781611974973.15}, PUBLISHER = {SIAM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017)}, EDITOR = {Chawla, Nitesh and Wang, Wei}, PAGES = {126--134}, ADDRESS = {Houston, TX, USA}, }
Endnote
%0 Conference Proceedings %A Roel, Bertens %A Vreeken, Jilles %A Siebes, Arno %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Efficiently Discovering Unexpected Pattern-Co-Occurrences : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-066E-3 %R 10.1137/1.9781611974973.15 %D 2017 %B 17th SIAM International Conference on Data Mining %Z date of event: 2017-04-27 - 2017-04-29 %C Houston, TX, USA %B Proceedings of the Seventeenth SIAM International Conference on Data Mining %E Chawla, Nitesh; Wang, Wei %P 126 - 134 %I SIAM %@ 978-1-611974-87-4
[88]
A. Rohrbach, A. Torabi, M. Rohrbach, N. Tandon, C. Pal, H. Larochelle, A. Courville, and B. Schiele, “Movie Description,” International Journal of Computer Vision, vol. 123, no. 1, 2017.
Abstract
Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. In total the Large Scale Movie Description Challenge (LSMDC) contains a parallel corpus of 118,114 sentences and video clips from 202 movies. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are indeed more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in a challenge organized in the context of the workshop "Describing and Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at ICCV 2015.
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@article{RohrbachMovie, TITLE = {Movie Description}, AUTHOR = {Rohrbach, Anna and Torabi, Atousa and Rohrbach, Marcus and Tandon, Niket and Pal, Christopher and Larochelle, Hugo and Courville, Aaron and Schiele, Bernt}, LANGUAGE = {eng}, DOI = {10.1007/s11263-016-0987-1}, PUBLISHER = {Springer}, ADDRESS = {London}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, ABSTRACT = {Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. In total the Large Scale Movie Description Challenge (LSMDC) contains a parallel corpus of 118,114 sentences and video clips from 202 movies. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are indeed more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in a challenge organized in the context of the workshop "Describing and Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at ICCV 2015.}, JOURNAL = {International Journal of Computer Vision}, VOLUME = {123}, NUMBER = {1}, PAGES = {94--120}, }
Endnote
%0 Journal Article %A Rohrbach, Anna %A Torabi, Atousa %A Rohrbach, Marcus %A Tandon, Niket %A Pal, Christopher %A Larochelle, Hugo %A Courville, Aaron %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Movie Description : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-FD03-C %R 10.1007/s11263-016-0987-1 %7 2017-01-25 %D 2017 %X Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. In total the Large Scale Movie Description Challenge (LSMDC) contains a parallel corpus of 118,114 sentences and video clips from 202 movies. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are indeed more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in a challenge organized in the context of the workshop "Describing and Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at ICCV 2015. %K Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Computation and Language, cs.CL %J International Journal of Computer Vision %O IJCV %V 123 %N 1 %& 94 %P 94 - 120 %I Springer %C London
[89]
V. Setty, A. Anand, A. Mishra, and A. Anand, “Modeling Event Importance for Ranking Daily News Events,” in WSDM’17, 10th ACM International Conference on Web Search and Data Mining, Cambridge, UK, 2017.
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@inproceedings{Setii2017, TITLE = {Modeling Event Importance for Ranking Daily News Events}, AUTHOR = {Setty, Vinay and Anand, Abhijit and Mishra, Arunav and Anand, Avishek}, LANGUAGE = {eng}, ISBN = {978-1-4503-4675-7}, DOI = {10.1145/3018661.3018728}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WSDM'17, 10th ACM International Conference on Web Search and Data Mining}, PAGES = {231--240}, ADDRESS = {Cambridge, UK}, }
Endnote
%0 Conference Proceedings %A Setty, Vinay %A Anand, Abhijit %A Mishra, Arunav %A Anand, Avishek %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Modeling Event Importance for Ranking Daily News Events : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-26D5-9 %R 10.1145/3018661.3018728 %D 2017 %B 10th ACM International Conference on Web Search and Data Mining %Z date of event: 2017-02-06 - 2017-02-10 %C Cambridge, UK %B WSDM'17 %P 231 - 240 %I ACM %@ 978-1-4503-4675-7
[90]
D. Seyler, T. Dembelova, L. Del Corro, J. Hoffart, and G. Weikum, “KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition,” 2017. [Online]. Available: http://arxiv.org/abs/1709.03544. (arXiv: 1709.03544)
Abstract
KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources (such as a knowledge-base, a list of names or document-specific semantic annotations) and is used to train a conditional random field (CRF). Since those information sources are usually multilingual, KnowNER can be easily trained for a wide range of languages. In this paper, we show that the incorporation of deeper knowledge systematically boosts accuracy and compare KnowNER with state-of-the-art NER approaches across three languages (i.e., English, German and Spanish) performing amongst state-of-the art systems in all of them.
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@online{Seyler_arXiv2017, TITLE = {{KnowNER}: Incremental Multilingual {Knowledge} in {Named Entity Recognition}}, AUTHOR = {Seyler, Dominic and Dembelova, Tatiana and Del Corro, Luciano and Hoffart, Johannes and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1709.03544}, EPRINT = {1709.03544}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources (such as a knowledge-base, a list of names or document-specific semantic annotations) and is used to train a conditional random field (CRF). Since those information sources are usually multilingual, KnowNER can be easily trained for a wide range of languages. In this paper, we show that the incorporation of deeper knowledge systematically boosts accuracy and compare KnowNER with state-of-the-art NER approaches across three languages (i.e., English, German and Spanish) performing amongst state-of-the art systems in all of them.}, }
Endnote
%0 Report %A Seyler, Dominic %A Dembelova, Tatiana %A Del Corro, Luciano %A Hoffart, Johannes %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-0693-D %U http://arxiv.org/abs/1709.03544 %D 2017 %X KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources (such as a knowledge-base, a list of names or document-specific semantic annotations) and is used to train a conditional random field (CRF). Since those information sources are usually multilingual, KnowNER can be easily trained for a wide range of languages. In this paper, we show that the incorporation of deeper knowledge systematically boosts accuracy and compare KnowNER with state-of-the-art NER approaches across three languages (i.e., English, German and Spanish) performing amongst state-of-the art systems in all of them. %K Computer Science, Computation and Language, cs.CL
[91]
D. Seyler, M. Yahya, and K. Berberich, “Knowledge Questions from Knowledge Graphs,” in ICTIR’17, 7th International Conference on the Theory of Information Retrieval, Amsterdam, The Netherlands, 2017.
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@inproceedings{SeylerICTIR2017, TITLE = {Knowledge Questions from Knowledge Graphs}, AUTHOR = {Seyler, Dominic and Yahya, Mohamed and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-4490-6}, DOI = {10.1145/3121050.3121073}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {ICTIR'17, 7th International Conference on the Theory of Information Retrieval}, PAGES = {11--18}, ADDRESS = {Amsterdam, The Netherlands}, }
Endnote
%0 Conference Proceedings %A Seyler, Dominic %A Yahya, Mohamed %A Berberich, Klaus %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowledge Questions from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-0647-A %R 10.1145/3121050.3121073 %D 2017 %B 7th International Conference on the Theory of Information Retrieval %Z date of event: 2017-10-01 - 2017-10-04 %C Amsterdam, The Netherlands %B ICTIR'17 %P 11 - 18 %I ACM %@ 978-1-4503-4490-6
[92]
L. Soldaini, A. Yates, and N. Goharian, “Learning to Reformulate Long Queries for Clinical Decision Support,” Journal of the Association for Information Science and Technology, vol. 68, no. 11, 2017.
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@article{Soldaini2017, TITLE = {Learning to Reformulate Long Queries for Clinical Decision Support}, AUTHOR = {Soldaini, Luca and Yates, Andrew and Goharian, Nazli}, LANGUAGE = {eng}, ISSN = {2330-1635}, DOI = {10.1002/asi.23924}, PUBLISHER = {Wiley}, ADDRESS = {Chichester, UK}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Journal of the Association for Information Science and Technology}, VOLUME = {68}, NUMBER = {11}, PAGES = {2602--2619}, }
Endnote
%0 Journal Article %A Soldaini, Luca %A Yates, Andrew %A Goharian, Nazli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Learning to Reformulate Long Queries for Clinical Decision Support : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-2723-C %R 10.1002/asi.23924 %7 2017-09-14 %D 2017 %8 14.09.2017 %J Journal of the Association for Information Science and Technology %O asis&t %V 68 %N 11 %& 2602 %P 2602 - 2619 %I Wiley %C Chichester, UK %@ false
[93]
J. Stoyanovich, B. Howe, S. Abiteboul, G. Miklau, A. Sahuguet, and G. Weikum, “Fides: Towards a Platform for Responsible Data Science,” in 29th International Conference on Scientific and Statistical Database Management (SSDBM 2017), Chicago, IL, USA, 2017.
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@inproceedings{StoyanovichSSDBM2017, TITLE = {Fides: {T}owards a Platform for Responsible Data Science}, AUTHOR = {Stoyanovich, Julia and Howe, Bill and Abiteboul, Serge and Miklau, Gerome and Sahuguet, Arnaud and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-5282-6}, DOI = {10.1145/3085504.3085530}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {29th International Conference on Scientific and Statistical Database Management (SSDBM 2017)}, EID = {26}, ADDRESS = {Chicago, IL, USA}, }
Endnote
%0 Conference Proceedings %A Stoyanovich, Julia %A Howe, Bill %A Abiteboul, Serge %A Miklau, Gerome %A Sahuguet, Arnaud %A Weikum, Gerhard %+ External Organizations External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Fides: Towards a Platform for Responsible Data Science : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-80BA-B %R 10.1145/3085504.3085530 %D 2017 %B 29th International Conference on Scientific and Statistical Database Management %Z date of event: 2017-06-27 - 2017-06-29 %C Chicago, IL, USA %B 29th International Conference on Scientific and Statistical Database Management %Z sequence number: 26 %I ACM %@ 978-1-4503-5282-6
[94]
N. Tandon, G. de Melo, and G. Weikum, “WebChild 2.0: Fine-Grained Commonsense Knowledge Distillation,” in The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, Canada, 2017.
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@inproceedings{TandonACL2017, TITLE = {{WebChild} 2.0: {F}ine-Grained Commonsense Knowledge Distillation}, AUTHOR = {Tandon, Niket and de Melo, Gerard and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-945626-76-0}, DOI = {10.18653/v1/P17-4020}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017)}, PAGES = {115--120}, ADDRESS = {Vancouver, Canada}, }
Endnote
%0 Conference Proceedings %A Tandon, Niket %A de Melo, Gerard %A Weikum, Gerhard %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T WebChild 2.0: Fine-Grained Commonsense Knowledge Distillation : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-FAC3-A %R 10.18653/v1/P17-4020 %D 2017 %B The 55th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2017-07-30 - 2017-08-04 %C Vancouver, Canada %B The 55th Annual Meeting of the Association for Computational Linguistics %P 115 - 120 %I ACL %@ 978-1-945626-76-0
[95]
C. Teflioudi and R. Gemulla, “Exact and Approximate Maximum Inner Product Search with LEMP,” ACM Transactions on Database Systems, vol. 42, no. 1, 2017.
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@article{Teflioudi:2016:EAM:3015779.2996452, TITLE = {Exact and Approximate Maximum Inner Product Search with {LEMP}}, AUTHOR = {Teflioudi, Christina and Gemulla, Rainer}, LANGUAGE = {eng}, ISSN = {0362-5915}, DOI = {10.1145/2996452}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, JOURNAL = {ACM Transactions on Database Systems}, VOLUME = {42}, NUMBER = {1}, EID = {5}, }
Endnote
%0 Journal Article %A Teflioudi, Christina %A Gemulla, Rainer %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Exact and Approximate Maximum Inner Product Search with LEMP : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-349C-B %R 10.1145/2996452 %7 2016 %D 2017 %J ACM Transactions on Database Systems %O TODS %V 42 %N 1 %Z sequence number: 5 %I ACM %C New York, NY %@ false
[96]
E. N. Toosi, “A New Efficient and Scalable Algorithm for Boolean Matrix Factorization,” Universität des Saarlandes, Saarbrücken, 2017.
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@mastersthesis{ToosiMsc2017, TITLE = {A New Efficient and Scalable Algorithm for {Boolean} Matrix Factorization}, AUTHOR = {Toosi, Ehsan Nadjaran}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Thesis %A Toosi, Ehsan Nadjaran %Y Miettinen, Pauli %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T A New Efficient and Scalable Algorithm for Boolean Matrix Factorization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-90D5-E %I Universität des Saarlandes %C Saarbrücken %D 2017 %P X, 70 p. %V master %9 master
[97]
H. D. Tran, D. Stepanova, M. Gad-Elrab, F. A. Lisi, and G. Weikum, “Towards Nonmonotonic Relational Learning from Knowledge Graphs,” in Inductive Logic Programming (ILP 2016), London, UK, 2017.
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@inproceedings{TranILP2016, TITLE = {Towards Nonmonotonic Relational Learning from Knowledge Graphs}, AUTHOR = {Tran, Hai Dang and Stepanova, Daria and Gad-Elrab, Mohamed and Lisi, Francesca A. and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-319-63341-1}, DOI = {10.1007/978-3-319-63342-8_8}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {Inductive Logic Programming (ILP 2016)}, EDITOR = {Cussens, James and Russo, Alessandra}, PAGES = {94--107}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {10326}, ADDRESS = {London, UK}, }
Endnote
%0 Conference Proceedings %A Tran, Hai Dang %A Stepanova, Daria %A Gad-Elrab, Mohamed %A Lisi, Francesca A. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Towards Nonmonotonic Relational Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2DB1-E %R 10.1007/978-3-319-63342-8_8 %D 2017 %B 26th International Conference on Inductive Logic Programming %Z date of event: 2016-09-04 - 2016-09-06 %C London, UK %B Inductive Logic Programming %E Cussens, James; Russo, Alessandra %P 94 - 107 %I Springer %@ 978-3-319-63341-1 %B Lecture Notes in Artificial Intelligence %N 10326
[98]
H. D. Tran, “An Approach to Nonmonotonic Relational Learning from Knowledge Graphs,” Universität des Saarlandes, Saarbrücken, 2017.
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@mastersthesis{TranMSc2017, TITLE = {An Approach to Nonmonotonic Relational Learning from Knowledge Graphs}, AUTHOR = {Tran, Hai Dang}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Thesis %A Tran, Hai Dang %Y Stepanova, Daria %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T An Approach to Nonmonotonic Relational Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-845A-3 %I Universität des Saarlandes %C Saarbrücken %D 2017 %P XV, 48 p. %V master %9 master
[99]
G. Weikum, “What Computers Should Know, Shouldn’t Know, and Shouldn’t Believe,” in WWW’17 Companion, Perth, Australia, 2017.
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@inproceedings{WeikumWWW2017, TITLE = {What Computers Should Know, Shouldn{\textquoteright}t Know, and Shouldn{\textquoteright}t Believe}, AUTHOR = {Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4914-7}, DOI = {10.1145/3041021.3051120}, PUBLISHER = {ACM}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {WWW'17 Companion}, PAGES = {1559--1560}, ADDRESS = {Perth, Australia}, }
Endnote
%0 Conference Proceedings %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T What Computers Should Know, Shouldn’t Know, and Shouldn’t Believe : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-7DA0-5 %R 10.1145/3041021.3051120 %D 2017 %B 26th International Conference on World Wide Web %Z date of event: 2017-04-03 - 2017-04-07 %C Perth, Australia %B WWW'17 Companion %P 1559 - 1560 %I ACM %@ 978-1-4503-4914-7
[100]
A. Yates, A. Cohan, and N. Goharian, “Depression and Self-Harm Risk Assessment in Online Forums,” 2017. [Online]. Available: http://arxiv.org/abs/1709.01848. (arXiv: 1709.01848)
Abstract
Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.
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@online{Yates_arXiv2017b, TITLE = {Depression and Self-Harm Risk Assessment in Online Forums}, AUTHOR = {Yates, Andrew and Cohan, Arman and Goharian, Nazli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1709.01848}, EPRINT = {1709.01848}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.}, }
Endnote
%0 Report %A Yates, Andrew %A Cohan, Arman %A Goharian, Nazli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Depression and Self-Harm Risk Assessment in Online Forums : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-06C8-6 %U http://arxiv.org/abs/1709.01848 %D 2017 %X Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset. %K Computer Science, Computation and Language, cs.CL
[101]
A. Yates, A. Cohan, and N. Goharian, “Depression and Self-Harm Risk Assessment in Online Forums,” in The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, 2017.
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@inproceedings{YatesENMLP2017, TITLE = {Depression and Self-Harm Risk Assessment in Online Forums}, AUTHOR = {Yates, Andrew and Cohan, Arman and Goharian, Nazli}, LANGUAGE = {eng}, ISBN = {978-1-945626-83-8}, URL = {https://aclanthology.info/pdf/D/D17/D17-1321.pdf}, PUBLISHER = {ACL}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, BOOKTITLE = {The Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)}, PAGES = {2958--2968}, ADDRESS = {Copenhagen, Denmark}, }
Endnote
%0 Conference Proceedings %A Yates, Andrew %A Cohan, Arman %A Goharian, Nazli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Depression and Self-Harm Risk Assessment in Online Forums : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-06A0-D %U https://aclanthology.info/pdf/D/D17/D17-1321.pdf %D 2017 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2017-09-09 - 2017-09-11 %C Copenhagen, Denmark %B The Conference on Empirical Methods in Natural Language Processing %P 2958 - 2968 %I ACL %@ 978-1-945626-83-8 %U https://aclanthology.info/pdf/D/D17/D17-1321.pdf
[102]
A. Yates and K. Hui, “DE-PACRR: Exploring Layers Inside the PACRR Model,” 2017. [Online]. Available: http://arxiv.org/abs/1706.08746. (arXiv: 1706.08746)
Abstract
Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval. However, deep models have a reputation for being black boxes, and the roles of a neural IR model's components may not be obvious at first glance. In this work, we attempt to shed light on the inner workings of a recently proposed neural IR model, namely the PACRR model, by visualizing the output of intermediate layers and by investigating the relationship between intermediate weights and the ultimate relevance score produced. We highlight several insights, hoping that such insights will be generally applicable.
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@online{Yates_arXiv2017, TITLE = {{DE}-{PACRR}: Exploring Layers Inside the {PACRR} Model}, AUTHOR = {Yates, Andrew and Hui, Kai}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1706.08746}, EPRINT = {1706.08746}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval. However, deep models have a reputation for being black boxes, and the roles of a neural IR model's components may not be obvious at first glance. In this work, we attempt to shed light on the inner workings of a recently proposed neural IR model, namely the PACRR model, by visualizing the output of intermediate layers and by investigating the relationship between intermediate weights and the ultimate relevance score produced. We highlight several insights, hoping that such insights will be generally applicable.}, }
Endnote
%0 Report %A Yates, Andrew %A Hui, Kai %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T DE-PACRR: Exploring Layers Inside the PACRR Model : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002E-06BE-D %U http://arxiv.org/abs/1706.08746 %D 2017 %X Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval. However, deep models have a reputation for being black boxes, and the roles of a neural IR model's components may not be obvious at first glance. In this work, we attempt to shed light on the inner workings of a recently proposed neural IR model, namely the PACRR model, by visualizing the output of intermediate layers and by investigating the relationship between intermediate weights and the ultimate relevance score produced. We highlight several insights, hoping that such insights will be generally applicable. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[103]
Y. Zhang, M. Humbert, B. Surma, P. Manoharan, J. Vreeken, and M. Backes, “CTRL+Z: Recovering Anonymized Social Graphs,” 2017. [Online]. Available: http://arxiv.org/abs/1711.05441. (arXiv: 1711.05441)
Abstract
Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, due to the sensitivity of information contained in such social graphs, they need to be properly anonymized before release. Most of the graph anonymization techniques that have been proposed to sanitize social graph data rely on the perturbation of the original graph's structure, more specifically of its edge set. In this paper, we identify a fundamental weakness of these edge-based anonymization mechanisms and exploit it to recover most of the original graph structure. First, we propose a method to quantify an edge's plausibility in a given graph by relying on graph embedding. Our experiments on three real-life social network datasets under two widely known graph anonymization mechanisms demonstrate that this method can very effectively detect fake edges with AUC values above 0.95 in most cases. Second, by relying on Gaussian mixture models and maximum a posteriori probability estimation, we derive an optimal decision rule to detect whether an edge is fake based on the observed graph data. We further demonstrate that this approach concretely jeopardizes the privacy guarantees provided by the considered graph anonymization mechanisms. To mitigate this vulnerability, we propose a method to generate fake edges as plausible as possible given the graph structure and incorporate it into the existing anonymization mechanisms. Our evaluation demonstrates that the enhanced mechanisms not only decrease the chances of graph recovery (with AUC dropping by up to 35%), but also provide even better graph utility than existing anonymization methods.
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@online{Zhang1711.05441, TITLE = {{CTRL}+Z: Recovering Anonymized Social Graphs}, AUTHOR = {Zhang, Yang and Humbert, Mathias and Surma, Bartlomiej and Manoharan, Praveen and Vreeken, Jilles and Backes, Michael}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1711.05441}, EPRINT = {1711.05441}, EPRINTTYPE = {arXiv}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, due to the sensitivity of information contained in such social graphs, they need to be properly anonymized before release. Most of the graph anonymization techniques that have been proposed to sanitize social graph data rely on the perturbation of the original graph's structure, more specifically of its edge set. In this paper, we identify a fundamental weakness of these edge-based anonymization mechanisms and exploit it to recover most of the original graph structure. First, we propose a method to quantify an edge's plausibility in a given graph by relying on graph embedding. Our experiments on three real-life social network datasets under two widely known graph anonymization mechanisms demonstrate that this method can very effectively detect fake edges with AUC values above 0.95 in most cases. Second, by relying on Gaussian mixture models and maximum a posteriori probability estimation, we derive an optimal decision rule to detect whether an edge is fake based on the observed graph data. We further demonstrate that this approach concretely jeopardizes the privacy guarantees provided by the considered graph anonymization mechanisms. To mitigate this vulnerability, we propose a method to generate fake edges as plausible as possible given the graph structure and incorporate it into the existing anonymization mechanisms. Our evaluation demonstrates that the enhanced mechanisms not only decrease the chances of graph recovery (with AUC dropping by up to 35%), but also provide even better graph utility than existing anonymization methods.}, }
Endnote
%0 Report %A Zhang, Yang %A Humbert, Mathias %A Surma, Bartlomiej %A Manoharan, Praveen %A Vreeken, Jilles %A Backes, Michael %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T CTRL+Z: Recovering Anonymized Social Graphs : %G eng %U http://hdl.handle.net/21.11116/0000-0000-6463-0 %U http://arxiv.org/abs/1711.05441 %D 2017 %X Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, due to the sensitivity of information contained in such social graphs, they need to be properly anonymized before release. Most of the graph anonymization techniques that have been proposed to sanitize social graph data rely on the perturbation of the original graph's structure, more specifically of its edge set. In this paper, we identify a fundamental weakness of these edge-based anonymization mechanisms and exploit it to recover most of the original graph structure. First, we propose a method to quantify an edge's plausibility in a given graph by relying on graph embedding. Our experiments on three real-life social network datasets under two widely known graph anonymization mechanisms demonstrate that this method can very effectively detect fake edges with AUC values above 0.95 in most cases. Second, by relying on Gaussian mixture models and maximum a posteriori probability estimation, we derive an optimal decision rule to detect whether an edge is fake based on the observed graph data. We further demonstrate that this approach concretely jeopardizes the privacy guarantees provided by the considered graph anonymization mechanisms. To mitigate this vulnerability, we propose a method to generate fake edges as plausible as possible given the graph structure and incorporate it into the existing anonymization mechanisms. Our evaluation demonstrates that the enhanced mechanisms not only decrease the chances of graph recovery (with AUC dropping by up to 35%), but also provide even better graph utility than existing anonymization methods. %K Computer Science, Cryptography and Security, cs.CR,cs.SI
[104]
D. Ziegler, A. Abujabal, R. S. Roy, and G. Weikum, “Efficiency-aware Answering of Compositional Questions using Answer Type Prediction,” in The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), Taipei, Taiwan, 2017.
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@inproceedings{ZieglerIJCNLP2017, TITLE = {Efficiency-aware Answering of Compositional Questions using Answer Type Prediction}, AUTHOR = {Ziegler, David and Abujabal, Abdalghani and Roy, Rishiraj Saha and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-948087-01-8}, URL = {http://aclweb.org/anthology/I17-2038}, PUBLISHER = {Asian Federation of Natural Language Processing}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)}, PAGES = {222--227}, ADDRESS = {Taipei, Taiwan}, }
Endnote
%0 Conference Proceedings %A Ziegler, David %A Abujabal, Abdalghani %A Roy, Rishiraj Saha %A Weikum, Gerhard %+ Algorithms and Complexity, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Efficiency-aware Answering of Compositional Questions using Answer Type Prediction : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3B5F-5 %U http://aclweb.org/anthology/I17-2038 %D 2017 %B 8th International Joint Conference on Natural Language Processing %Z date of event: 2017-11-27 - 2017-12-01 %C Taipei, Taiwan %B The 8th International Joint Conference on Natural Language Processing %P 222 - 227 %I Asian Federation of Natural Language Processing %@ 978-1-948087-01-8
[105]
D. Ziegler, “Answer Type Prediction for Question Answering over Knowledge Bases,” Universität des Saarlandes, Saarbrücken, 2017.
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@mastersthesis{ZieglerMSc2017, TITLE = {Answer Type Prediction for Question Answering over Knowledge Bases}, AUTHOR = {Ziegler, David}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, DATE = {2017}, }
Endnote
%0 Thesis %A Ziegler, David %Y Abujabal, Abdalghani %A referee: Roy, Rishiraj Saha %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Answer Type Prediction for Question Answering over Knowledge Bases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-8F38-A %I Universität des Saarlandes %C Saarbrücken %D 2017 %P X, 48 p. %V master %9 master
2016
[106]
K. Athukorala, D. Głowack, G. Jacucci, A. Oulasvirta, and J. Vreeken, “Is Exploratory Search Different? A Comparison of Information Search Behavior for Exploratory and Lookup Tasks,” Journal of the Association for Information Science and Technology, vol. 67, no. 11, 2016.
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@article{VreekenSearch2015, TITLE = {Is Exploratory Search Different? {A} Comparison of Information Search Behavior for Exploratory and Lookup Tasks}, AUTHOR = {Athukorala, Kumaripaba and G{\l}owack, Dorota and Jacucci, Giulio and Oulasvirta, Antti and Vreeken, Jilles}, LANGUAGE = {eng}, ISSN = {2330-1643}, DOI = {10.1002/asi.23617}, PUBLISHER = {Wiley}, ADDRESS = {Chichester}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, JOURNAL = {Journal of the Association for Information Science and Technology}, VOLUME = {67}, NUMBER = {11}, PAGES = {2635--2651}, }
Endnote
%0 Journal Article %A Athukorala, Kumaripaba %A Głowack, Dorota %A Jacucci, Giulio %A Oulasvirta, Antti %A Vreeken, Jilles %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Is Exploratory Search Different? A Comparison of Information Search Behavior for Exploratory and Lookup Tasks : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-E6A7-D %R 10.1002/asi.23617 %7 2015-10-22 %D 2016 %J Journal of the Association for Information Science and Technology %V 67 %N 11 %& 2635 %P 2635 - 2651 %I Wiley %C Chichester %@ false
[107]
A. H. Baradaranshahroudi, “Fast Computation of Highest Correlated Segments in Multivariate Time-Series,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{BaradaranshahroudiMSc2016, TITLE = {Fast Computation of Highest Correlated Segments in Multivariate Time-Series}, AUTHOR = {Baradaranshahroudi, Amir Hossein}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Baradaranshahroudi, Amir Hossein %Y Vreeken, Jilles %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Fast Computation of Highest Correlated Segments in Multivariate Time-Series : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5FB1-1 %I Universität des Saarlandes %C Saarbrücken %D 2016 %V master %9 master
[108]
B. Berendt, B. Bringmann, E. Fromont, G. Garriga, P. Miettinen, N. Tatti, and V. Tresp, Eds., Machine Learning and Knowledge Discovery in Databases. Springer, 2016.
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@proceedings{ProceedingsECML2016III, TITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016)}, EDITOR = {Berendt, Bettina and Bringmann, Bj{\"o}rn and Fromont, Elisa and Garriga, Gemma and Miettinen, Pauli and Tatti, Nikolai and Tresp, Volker}, LANGUAGE = {eng}, ISBN = {978-3-319-46130-4}, DOI = {10.1007/978-3-319-46131-1}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, PAGES = {XXII, 307 p.}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {9853}, ADDRESS = {Riva del Garda, Italy}, }
Endnote
%0 Conference Proceedings %E Berendt, Bettina %E Bringmann, Björn %E Fromont, Elisa %E Garriga, Gemma %E Miettinen, Pauli %E Tatti, Nikolai %E Tresp, Volker %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2016 ; Riva del Garda, Italy, September 19-23, 2016 ; Proceedings, Part III %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A68E-5 %R 10.1007/978-3-319-46131-1 %@ 978-3-319-46130-4 %I Springer %D 2016 %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases %Z date of event: 2016-09-19 - 2016-09-23 %D 2016 %C Riva del Garda, Italy %P XXII, 307 p. %S Lecture Notes in Artificial Intelligence %V 9853
[109]
R. Bertens, J. Vreeken, and A. Siebes, “Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns,” in KDD’16, 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 735–744.
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@inproceedings{BertensKDD2016, TITLE = {Keeping it Short and Simple: {S}ummarising Complex Event Sequences with Multivariate Patterns}, AUTHOR = {Bertens, Roel and Vreeken, Jilles and Siebes, Arno}, LANGUAGE = {eng}, ISBN = {978-1-4503-4232-2}, DOI = {10.1145/2939672.2939761}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {KDD'16, 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, PAGES = {735--744}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Bertens, Roel %A Vreeken, Jilles %A Siebes, Arno %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A92D-B %R 10.1145/2939672.2939761 %D 2016 %B 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining %Z date of event: 2016-08-13 - 2016-08-17 %C San Francisco, CA, USA %B KDD'16 %P 735 - 744 %I ACM %@ 978-1-4503-4232-2
[110]
A. Bhattacharyya, “Squish: Efficiently Summarising Sequences with Rich and Interleaving Patterns,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{BhattacharyyaMSc2016, TITLE = {Squish: Efficiently Summarising Sequences with Rich and Interleaving Patterns}, AUTHOR = {Bhattacharyya, Apratim}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Bhattacharyya, Apratim %Y Vreeken, Jilles %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Squish: Efficiently Summarising Sequences with Rich and Interleaving Patterns : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5F37-4 %I Universität des Saarlandes %C Saarbrücken %D 2016 %V master %9 master
[111]
J. A. Biega, K. P. Gummadi, I. Mele, D. Milchevski, C. Tryfonopoulos, and G. Weikum, “R-Susceptibility: An IR-Centric Approach to Assessing Privacy Risks for Users in Online Communities,” in SIGIR’16, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, 2016.
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@inproceedings{BiegaSIGIR2016, TITLE = {R-Susceptibility: {A}n {IR}-Centric Approach to Assessing Privacy Risks for Users in Online Communities}, AUTHOR = {Biega, Joanna Asia and Gummadi, Krishna P. and Mele, Ida and Milchevski, Dragan and Tryfonopoulos, Christos and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4069-4}, DOI = {10.1145/2911451.2911533}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {SIGIR'16, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {365--374}, ADDRESS = {Pisa, Italy}, }
Endnote
%0 Conference Proceedings %A Biega, Joanna Asia %A Gummadi, Krishna P. %A Mele, Ida %A Milchevski, Dragan %A Tryfonopoulos, Christos %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T R-Susceptibility: An IR-Centric Approach to Assessing Privacy Risks for Users in Online Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A921-3 %R 10.1145/2911451.2911533 %D 2016 %B 39th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2016-07-17 - 2016-07-21 %C Pisa, Italy %B SIGIR'16 %P 365 - 374 %I ACM %@ 978-1-4503-4069-4
[112]
N. Boldyrev, M. Spaniol, and G. Weikum, “ACROSS: A Framework for Multi-Cultural Interlinking of Web Taxonomies,” in WebSci’16, ACM Web Science Conference, Hannover, Germany, 2016.
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@inproceedings{BoldryevWebSci2016, TITLE = {{ACROSS}: {A} Framework for Multi-Cultural Interlinking of {W}eb Taxonomies}, AUTHOR = {Boldyrev, Natalia and Spaniol, Marc and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4208-7}, DOI = {10.1145/2908131.2908164}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WebSci'16, ACM Web Science Conference}, PAGES = {127--136}, ADDRESS = {Hannover, Germany}, }
Endnote
%0 Conference Proceedings %A Boldyrev, Natalia %A Spaniol, Marc %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T ACROSS: A Framework for Multi-Cultural Interlinking of Web Taxonomies : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-01B6-E %R 10.1145/2908131.2908164 %D 2016 %B ACM Web Science Conference %Z date of event: 2016-05-22 - 2016-05-25 %C Hannover, Germany %B WebSci'16 %P 127 - 136 %I ACM %@ 978-1-4503-4208-7
[113]
C. X. Chu, “Mining How-to Task Knowledge from Online Communities,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{ChuMSc2016, TITLE = {Mining How-to Task Knowledge from Online Communities}, AUTHOR = {Chu, Cuong Xuan}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Chu, Cuong Xuan %Y Weikum, Gerhard %A referee: Vreeken, Jilles %A referee: Tandon, Niket %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mining How-to Task Knowledge from Online Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-491D-B %I Universität des Saarlandes %C Saarbrücken %D 2016 %P 66 p. %V master %9 master
[114]
L. Del Corro, “Methods for Open Information Extraction and Sense Disambiguation on Natural Language Text,” Universität des Saarlandes, Saarbrücken, 2016.
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@phdthesis{delcorrophd15, TITLE = {Methods for Open Information Extraction and Sense Disambiguation on Natural Language Text}, AUTHOR = {Del Corro, Luciano}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Del Corro, Luciano %Y Gemulla, Rainer %A referee: Ponzetto, Simone Paolo %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Methods for Open Information Extraction and Sense Disambiguation on Natural Language Text : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-B3DB-3 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P xiv, 101 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/volltexte/2016/6346/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[115]
G. de Melo and N. Tandon, “Seeing is Believing: The Quest for Multimodal Knowledge,” ACM SIGWEB Newsletter, no. Spring, 2016.
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@article{DemeloTandon:SIGWEB2016, TITLE = {Seeing is Believing: {T}he Quest for Multimodal Knowledge}, AUTHOR = {de Melo, Gerard and Tandon, Niket}, LANGUAGE = {eng}, DOI = {10.1145/2903513.2903517}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, JOURNAL = {ACM SIGWEB Newsletter}, NUMBER = {Spring}, EID = {4}, }
Endnote
%0 Journal Article %A de Melo, Gerard %A Tandon, Niket %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Seeing is Believing: The Quest for Multimodal Knowledge : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-54BB-3 %R 10.1145/2903513.2903517 %7 2016 %D 2016 %J ACM SIGWEB Newsletter %N Spring %Z sequence number: 4 %I ACM %C New York, NY
[116]
H. Dombrowski, “Boolean Tensor Decomposition based on the Walk’n'Merge Algorithm,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{DombrowskiMaster2016, TITLE = {Boolean Tensor Decomposition based on the Walk'n'Merge Algorithm}, AUTHOR = {Dombrowski, Helge}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Dombrowski, Helge %Y Miettinen, Pauli %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Boolean Tensor Decomposition based on the Walk'n'Merge Algorithm : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2280-1 %I Universität des Saarlandes %C Saarbrücken %D 2016 %V master %9 master
[117]
X. Du, O. Emebo, A. Varde, N. Tandon, S. N. Chowdhury, and G. Weikum, “Air Quality Assessment from Social Media and Structured Data: Pollutants and Health Impacts in Urban Planning,” in Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW 2016), Helsinki, Finland, 2016.
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@inproceedings{DuICDEW2016, TITLE = {Air Quality Assessment from Social Media and Structured Data: {P}ollutants and Health Impacts in Urban Planning}, AUTHOR = {Du, Xu and Emebo, Onyeka and Varde, Aparna and Tandon, Niket and Chowdhury, Sreyasi Nag and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1109/ICDEW.2016.7495616}, PUBLISHER = {IEEE}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW 2016)}, PAGES = {54--59}, ADDRESS = {Helsinki, Finland}, }
Endnote
%0 Conference Proceedings %A Du, Xu %A Emebo, Onyeka %A Varde, Aparna %A Tandon, Niket %A Chowdhury, Sreyasi Nag %A Weikum, Gerhard %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Air Quality Assessment from Social Media and Structured Data: Pollutants and Health Impacts in Urban Planning : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-01AE-2 %R 10.1109/ICDEW.2016.7495616 %D 2016 %B IEEE 32nd International Conference on Data Engineering Workshops %Z date of event: 2016-05-16 - 2016-05-20 %C Helsinki, Finland %B Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering Workshops %P 54 - 59 %I IEEE
[118]
P. Ernst, A. Siu, D. Milchevski, J. Hoffart, and G. Weikum, “DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences,” in Proceedings of ACL-2016 System Demonstrations, Berlin, Germany, 2016.
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@inproceedings{ernst-EtAl:2016:P16-4, TITLE = {{DeepLife:} {A}n Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences}, AUTHOR = {Ernst, Patrick and Siu, Amy and Milchevski, Dragan and Hoffart, Johannes and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-945626-0}, DOI = {10.18653/v1/P16-4004}, PUBLISHER = {ACL}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of ACL-2016 System Demonstrations}, EDITOR = {Pradhan, Sameer and Apidianaki, Marianna}, PAGES = {19--24}, ADDRESS = {Berlin, Germany}, }
Endnote
%0 Conference Proceedings %A Ernst, Patrick %A Siu, Amy %A Milchevski, Dragan %A Hoffart, Johannes %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-24CA-F %R 10.18653/v1/P16-4004 %D 2016 %B The 54th Annual Meeting of the Association for Computational Linguistics %Z date of event: 2016-08-07 - 2016-08-12 %C Berlin, Germany %B Proceedings of ACL-2016 System Demonstrations %E Pradhan, Sameer; Apidianaki, Marianna %P 19 - 24 %I ACL %@ 978-1-945626-0
[119]
M. H. Gad-Elrab, D. Stepanova, J. Urbani, and G. Weikum, “Exception-Enriched Rule Learning from Knowledge Graphs,” in KI 2016: Advances in Artificial Intelligence, Klagenfurt, Austria, 2016.
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@inproceedings{Gad-ElrabKI2016, TITLE = {Exception-Enriched Rule Learning from Knowledge Graphs}, AUTHOR = {Gad-Elrab, Mohamed H. and Stepanova, Daria and Urbani, Jacopo and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-319-46072-7}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {KI 2016: Advances in Artificial Intelligence}, EDITOR = {Friedrich, Gerhard and Helmert, Malte and Wotawa, Franz}, PAGES = {211--217}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {9904}, ADDRESS = {Klagenfurt, Austria}, }
Endnote
%0 Conference Proceedings %A Gad-Elrab, Mohamed H. %A Stepanova, Daria %A Urbani, Jacopo %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exception-Enriched Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-22E9-A %D 2016 %B 39th Annual German Conference on AI %Z date of event: 2016-09-26 - 2016-09-30 %C Klagenfurt, Austria %B KI 2016: Advances in Artificial Intelligence %E Friedrich, Gerhard; Helmert, Malte; Wotawa, Franz %P 211 - 217 %I Springer %@ 978-3-319-46072-7 %B Lecture Notes in Artificial Intelligence %N 9904
[120]
M. H. Gad-Elrab, D. Stepanova, J. Urbani, and G. Weikum, “Exception-Enriched Rule Learning from Knowledge Graphs,” in The Semantic Web -- ISWC 2016, Kobe, Japan, 2016.
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@inproceedings{Gad-ElrabISWC2016, TITLE = {Exception-Enriched Rule Learning from Knowledge Graphs}, AUTHOR = {Gad-Elrab, Mohamed H. and Stepanova, Daria and Urbani, Jacopo and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-319-46522-7}, DOI = {10.1007/978-3-319-46523-4_15}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {The Semantic Web -- ISWC 2016}, EDITOR = {Groth, Paul and Simperl, Elena and Gray, Alasdair and Sabou, Marta and Kr{\"o}tzsch, Markus and Lecue, Freddy and Fl{\"o}ck, Fabian and Gil, Yolanda}, PAGES = {234--251}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9981}, ADDRESS = {Kobe, Japan}, }
Endnote
%0 Conference Proceedings %A Gad-Elrab, Mohamed H. %A Stepanova, Daria %A Urbani, Jacopo %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exception-Enriched Rule Learning from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A91F-B %R 10.1007/978-3-319-46523-4_15 %D 2016 %B 15th International Semantic Web Conference %Z date of event: 2016-10-17 - 2016-10-21 %C Kobe, Japan %B The Semantic Web -- ISWC 2016 %E Groth, Paul; Simperl, Elena; Gray, Alasdair; Sabou, Marta; Krötzsch, Markus; Lecue, Freddy; Flöck, Fabian; Gil, Yolanda %P 234 - 251 %I Springer %@ 978-3-319-46522-7 %B Lecture Notes in Computer Science %N 9981
[121]
E. Galbrun and P. Miettinen, “Mining Redescriptions with Siren,” ACM Transactions on Knowledge Discovery from Data. (Accepted/in press)
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@article{galbrun17mining, TITLE = {Mining Redescriptions with {Siren}}, AUTHOR = {Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2016}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, JOURNAL = {ACM Transactions on Knowledge Discovery from Data}, }
Endnote
%0 Journal Article %A Galbrun, Esther %A Miettinen, Pauli %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mining Redescriptions with Siren : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-227B-F %D 2016 %J ACM Transactions on Knowledge Discovery from Data %I ACM %C New York, NY
[122]
M. Gandhi, “Towards Summarising Large Transaction Databases,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{GandhiMSc2016, TITLE = {Towards Summarising Large Transaction Databases}, AUTHOR = {Gandhi, Manan}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Gandhi, Manan %Y Vreeken, Jilles %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Towards Summarising Large Transaction Databases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5F61-4 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P X, 55 p. %V master %9 master
[123]
K. Grosse, “An Approach for Ontological Pattern-based Summarization,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{GrosseMSc2016, TITLE = {An Approach for Ontological Pattern-based Summarization}, AUTHOR = {Grosse, Kathrin}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Grosse, Kathrin %Y Vreeken, Jilles %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T An Approach for Ontological Pattern-based Summarization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5F5F-C %I Universität des Saarlandes %C Saarbrücken %D 2016 %P X, 84 p. %V master %9 master
[124]
A. Grycner and G. Weikum, “POLY: Mining Relational Paraphrases from Multilingual Sentences,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, TX, USA, 2016.
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@inproceedings{GrycnerENMLP2016, TITLE = {{POLY}: {M}ining Relational Paraphrases from Multilingual Sentences}, AUTHOR = {Grycner, Adam and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-945626-25-8}, URL = {https://aclweb.org/anthology/D16-1236}, PUBLISHER = {ACL}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP 2016)}, PAGES = {2183--2192}, ADDRESS = {Austin, TX, USA}, }
Endnote
%0 Conference Proceedings %A Grycner, Adam %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T POLY: Mining Relational Paraphrases from Multilingual Sentences : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-158D-0 %U https://aclweb.org/anthology/D16-1236 %D 2016 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2016-11-01 - 2016-11-05 %C Austin, TX, USA %B Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing %P 2183 - 2192 %I ACL %@ 978-1-945626-25-8
[125]
D. Gupta, “Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search and Analytics,” 2016. [Online]. Available: http://arxiv.org/abs/1603.00260. (arXiv: 1603.00260)
Abstract
In this article, I present the questions that I seek to answer in my PhD research. I posit to analyze natural language text with the help of semantic annotations and mine important events for navigating large text corpora. Semantic annotations such as named entities, geographic locations, and temporal expressions can help us mine events from the given corpora. These events thus provide us with useful means to discover the locked knowledge in them. I pose three problems that can help unlock this knowledge vault in semantically annotated text corpora: i. identifying important events; ii. semantic search; and iii. event analytics.
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@online{Gupta1603.00260, TITLE = {Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search and Analytics}, AUTHOR = {Gupta, Dhruv}, URL = {http://arxiv.org/abs/1603.00260}, DOI = {10.1145/2835776.2855083}, EPRINT = {1603.00260}, EPRINTTYPE = {arXiv}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, ABSTRACT = {In this article, I present the questions that I seek to answer in my PhD research. I posit to analyze natural language text with the help of semantic annotations and mine important events for navigating large text corpora. Semantic annotations such as named entities, geographic locations, and temporal expressions can help us mine events from the given corpora. These events thus provide us with useful means to discover the locked knowledge in them. I pose three problems that can help unlock this knowledge vault in semantically annotated text corpora: i. identifying important events; ii. semantic search; and iii. event analytics.}, }
Endnote
%0 Report %A Gupta, Dhruv %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search and Analytics : %U http://hdl.handle.net/11858/00-001M-0000-002C-2224-4 %R 10.1145/2835776.2855083 %U http://arxiv.org/abs/1603.00260 %D 2016 %X In this article, I present the questions that I seek to answer in my PhD research. I posit to analyze natural language text with the help of semantic annotations and mine important events for navigating large text corpora. Semantic annotations such as named entities, geographic locations, and temporal expressions can help us mine events from the given corpora. These events thus provide us with useful means to discover the locked knowledge in them. I pose three problems that can help unlock this knowledge vault in semantically annotated text corpora: i. identifying important events; ii. semantic search; and iii. event analytics. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
[126]
D. Gupta, “Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search & Analytics,” in WSDM’16, 9th ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA, 2016.
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@inproceedings{GuptaWSDM2016, TITLE = {Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search \& Analytics}, AUTHOR = {Gupta, Dhruv}, LANGUAGE = {eng}, ISBN = {978-1-4503-3716-8}, DOI = {10.1145/2835776.2855083}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WSDM'16, 9th ACM International Conference on Web Search and Data Mining}, PAGES = {705--705}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Event Search and Analytics: Detecting Events in Semantically Annotated Corpora for Search & Analytics : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-7526-7 %R 10.1145/2835776.2855083 %D 2016 %B 9th ACM International Conference on Web Search and Data Mining %Z date of event: 2016-02-22 - 2016-02-25 %C San Francisco, CA, USA %B WSDM'16 %P 705 - 705 %I ACM %@ 978-1-4503-3716-8
[127]
D. Gupta and K. Berberich, “Diversifying Search Results Using Time: An Information Retrieval Method for Historians,” in Advances in Information Retrieval (ECIR 2016), Padova, Italy, 2016.
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@inproceedings{GuptaECIR2016, TITLE = {Diversifying Search Results Using Time: An Information Retrieval Method for Historians}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-30670-4}, DOI = {10.1007/978-3-319-30671-1_69}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2016)}, EDITOR = {Ferro, Nicola and Crestani, Fabio and Moens, Marie-Francine and Mothe, Josiane and Silvestre, Fabrizio and Di Nunzio, Giorgio Maria and Hauff, Claudia and Silvello, Gianmaria}, PAGES = {789--795}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9626}, ADDRESS = {Padova, Italy}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Diversifying Search Results Using Time: An Information Retrieval Method for Historians : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-7514-F %R 10.1007/978-3-319-30671-1_69 %D 2016 %B 38th European Conference on Information Retrieval %Z date of event: 2016-03-20 - 2016-03-23 %C Padova, Italy %B Advances in Information Retrieval %E Ferro, Nicola; Crestani, Fabio; Moens, Marie-Francine; Mothe, Josiane; Silvestre, Fabrizio; Di Nunzio, Giorgio Maria; Hauff, Claudia; Silvello, Gianmaria %P 789 - 795 %I Springer %@ 978-3-319-30670-4 %B Lecture Notes in Computer Science %N 9626
[128]
D. Gupta and K. Berberich, “A Probabilistic Framework for Time-Sensitive Search,” in Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies, Tokyo, Japan, 2016.
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@inproceedings{GuptaNTCIR12, TITLE = {A Probabilistic Framework for Time-Sensitive Search}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-4-86049-071-3}, URL = {http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings12/NTCIR/toc_ntcir.html}, PUBLISHER = {National Institute of Informatics}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies}, DEBUG = {author: Yamamoto, Shuhei}, EDITOR = {Kando, Noriko and Kishida, Kazuaki and Kato, Makoto P.}, PAGES = {225--232}, ADDRESS = {Tokyo, Japan}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T A Probabilistic Framework for Time-Sensitive Search : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2238-7 %U http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings12/NTCIR/toc_ntcir.html %D 2016 %B 12th NTCIR Conference on Evaluation of Information Access Technologies %Z date of event: 2016-06-07 - 2016-06-10 %C Tokyo, Japan %B Proceedings of the 12th NTCIR Conference on Evaluation of Information Access Technologies %E Kando, Noriko; Kishida, Kazuaki; Kato, Makoto P.; Yamamoto, Shuhei %P 225 - 232 %I National Institute of Informatics %@ 978-4-86049-071-3
[129]
D. Gupta and K. Berberich, “Diversifying Search Results Using Time,” Max-Planck-Institut für Informatik, Saarbrücken, MPI-I-2016-5-001, 2016.
Abstract
Getting an overview of a historic entity or event can be difficult in search results, especially if important dates concerning the entity or event are not known beforehand. For such information needs, users would benefit if returned results covered diverse dates, thus giving an overview of what has happened throughout history. Diversifying search results based on important dates can be a building block for applications, for instance, in digital humanities. Historians would thus be able to quickly explore longitudinal document collections by querying for entities or events without knowing associated important dates apriori. In this work, we describe an approach to diversify search results using temporal expressions (e.g., in the 1990s) from their contents. Our approach first identifies time intervals of interest to the given keyword query based on pseudo-relevant documents. It then re-ranks query results so as to maximize the coverage of identified time intervals. We present a novel and objective evaluation for our proposed approach. We test the effectiveness of our methods on the New York Times Annotated corpus and the Living Knowledge corpus, collectively consisting of around 6 million documents. Using history-oriented queries and encyclopedic resources we show that our method indeed is able to present search results diversified along time.
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@techreport{GuptaReport2016-5-001, TITLE = {Diversifying Search Results Using Time}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISSN = {0946-011X}, NUMBER = {MPI-I-2016-5-001}, INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Getting an overview of a historic entity or event can be difficult in search results, especially if important dates concerning the entity or event are not known beforehand. For such information needs, users would benefit if returned results covered diverse dates, thus giving an overview of what has happened throughout history. Diversifying search results based on important dates can be a building block for applications, for instance, in digital humanities. Historians would thus be able to quickly explore longitudinal document collections by querying for entities or events without knowing associated important dates apriori. In this work, we describe an approach to diversify search results using temporal expressions (e.g., in the 1990s) from their contents. Our approach first identifies time intervals of interest to the given keyword query based on pseudo-relevant documents. It then re-ranks query results so as to maximize the coverage of identified time intervals. We present a novel and objective evaluation for our proposed approach. We test the effectiveness of our methods on the New York Times Annotated corpus and the Living Knowledge corpus, collectively consisting of around 6 million documents. Using history-oriented queries and encyclopedic resources we show that our method indeed is able to present search results diversified along time.}, TYPE = {Research Report}, }
Endnote
%0 Report %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Diversifying Search Results Using Time : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-0AA4-C %Y Max-Planck-Institut für Informatik %C Saarbrücken %D 2016 %P 51 p. %X Getting an overview of a historic entity or event can be difficult in search results, especially if important dates concerning the entity or event are not known beforehand. For such information needs, users would benefit if returned results covered diverse dates, thus giving an overview of what has happened throughout history. Diversifying search results based on important dates can be a building block for applications, for instance, in digital humanities. Historians would thus be able to quickly explore longitudinal document collections by querying for entities or events without knowing associated important dates apriori. In this work, we describe an approach to diversify search results using temporal expressions (e.g., in the 1990s) from their contents. Our approach first identifies time intervals of interest to the given keyword query based on pseudo-relevant documents. It then re-ranks query results so as to maximize the coverage of identified time intervals. We present a novel and objective evaluation for our proposed approach. We test the effectiveness of our methods on the New York Times Annotated corpus and the Living Knowledge corpus, collectively consisting of around 6 million documents. Using history-oriented queries and encyclopedic resources we show that our method indeed is able to present search results diversified along time. %B Research Report %@ false
[130]
D. Gupta, J. Strötgen, and K. Berberich, “DIGITALHISTORIAN: Search & Analytics Using Annotations,” in HistoInformatics 2016, The 3rd HistoInformatics Workshop on Computational History, Krakow, Poland, 2016.
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@inproceedings{Gupta, TITLE = {{DIGITALHISTORIAN}: {S}earch \& Analytics Using Annotations}, AUTHOR = {Gupta, Dhruv and Str{\"o}tgen, Jannik and Berberich, Klaus}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {urn:nbn:de:0074-1632-7}, PUBLISHER = {CEUR-WS.org}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {HistoInformatics 2016, The 3rd HistoInformatics Workshop on Computational History}, EDITOR = {D{\"u}ring, Marten and Jatowt, Adam and Preiser-Kappeller, Johannes and van Den Bosch, Antal}, PAGES = {5--10}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {1632}, ADDRESS = {Krakow, Poland}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Strötgen, Jannik %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T DIGITALHISTORIAN: Search & Analytics Using Annotations : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-0885-2 %D 2016 %B The 3rd HistoInformatics Workshop on Computational History %Z date of event: 2016-07-11 - 2016-07-11 %C Krakow, Poland %B HistoInformatics 2016 %E Düring, Marten; Jatowt, Adam; Preiser-Kappeller, Johannes; van Den Bosch, Antal %P 5 - 10 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 1632 %@ false %U http://ceur-ws.org/Vol-1632/paper_1.pdf
[131]
D. Gupta, J. Strötgen, and K. Berberich, “EventMiner: Mining Events from Annotated Documents,” in ICTIR’2016, ACM International Conference on the Theory of Information Retrieval, Newark, DE, USA, 2016.
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@inproceedings{GuptaICTIR2016, TITLE = {{EventMiner}: {M}ining Events from Annotated Documents}, AUTHOR = {Gupta, Dhruv and Str{\"o}tgen, Jannik and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-4497-5}, DOI = {10.1145/2970398.2970411}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {ICTIR'2016, ACM International Conference on the Theory of Information Retrieval}, PAGES = {261--270}, ADDRESS = {Newark, DE, USA}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Strötgen, Jannik %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T EventMiner: Mining Events from Annotated Documents : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-B262-0 %R 10.1145/2970398.2970411 %D 2016 %B ACM International Conference on the Theory of Information Retrieval %Z date of event: 2016-09-12 - 2016-09-16 %C Newark, DE, USA %B ICTIR'2016 %P 261 - 270 %I ACM %@ 978-1-4503-4497-5
[132]
S. Gurajada and M. Theobald, “Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1,” 2016. [Online]. Available: http://arxiv.org/abs/1609.05293. (arXiv: 1609.05293)
Abstract
We propose an efficient and scalable architecture for processing generalized graph-pattern queries as they are specified by the current W3C recommendation of the SPARQL 1.1 "Query Language" component. Specifically, the class of queries we consider consists of sets of SPARQL triple patterns with labeled property paths. From a relational perspective, this class resolves to conjunctive queries of relational joins with additional graph-reachability predicates. For the scalable, i.e., distributed, processing of this kind of queries over very large RDF collections, we develop a suitable partitioning and indexing scheme, which allows us to shard the RDF triples over an entire cluster of compute nodes and to process an incoming SPARQL query over all of the relevant graph partitions (and thus compute nodes) in parallel. Unlike most prior works in this field, we specifically aim at the unified optimization and distributed processing of queries consisting of both relational joins and graph-reachability predicates. All communication among the compute nodes is established via a proprietary, asynchronous communication protocol based on the Message Passing Interface.
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@online{Gurajada1609.05293, TITLE = {Distributed Processing of Generalized Graph-Pattern Queries in {SPARQL} 1.1}, AUTHOR = {Gurajada, Sairam and Theobald, Martin}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1609.05293}, EPRINT = {1609.05293}, EPRINTTYPE = {arXiv}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We propose an efficient and scalable architecture for processing generalized graph-pattern queries as they are specified by the current W3C recommendation of the SPARQL 1.1 "Query Language" component. Specifically, the class of queries we consider consists of sets of SPARQL triple patterns with labeled property paths. From a relational perspective, this class resolves to conjunctive queries of relational joins with additional graph-reachability predicates. For the scalable, i.e., distributed, processing of this kind of queries over very large RDF collections, we develop a suitable partitioning and indexing scheme, which allows us to shard the RDF triples over an entire cluster of compute nodes and to process an incoming SPARQL query over all of the relevant graph partitions (and thus compute nodes) in parallel. Unlike most prior works in this field, we specifically aim at the unified optimization and distributed processing of queries consisting of both relational joins and graph-reachability predicates. All communication among the compute nodes is established via a proprietary, asynchronous communication protocol based on the Message Passing Interface.}, }
Endnote
%0 Report %A Gurajada, Sairam %A Theobald, Martin %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1 : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2212-C %U http://arxiv.org/abs/1609.05293 %D 2016 %X We propose an efficient and scalable architecture for processing generalized graph-pattern queries as they are specified by the current W3C recommendation of the SPARQL 1.1 "Query Language" component. Specifically, the class of queries we consider consists of sets of SPARQL triple patterns with labeled property paths. From a relational perspective, this class resolves to conjunctive queries of relational joins with additional graph-reachability predicates. For the scalable, i.e., distributed, processing of this kind of queries over very large RDF collections, we develop a suitable partitioning and indexing scheme, which allows us to shard the RDF triples over an entire cluster of compute nodes and to process an incoming SPARQL query over all of the relevant graph partitions (and thus compute nodes) in parallel. Unlike most prior works in this field, we specifically aim at the unified optimization and distributed processing of queries consisting of both relational joins and graph-reachability predicates. All communication among the compute nodes is established via a proprietary, asynchronous communication protocol based on the Message Passing Interface. %K Computer Science, Databases, cs.DB
[133]
S. Gurajada and M. Theobald, “Distributed Set Reachability,” in SIGMOD’16, ACM SIGMOD International Conference on Management of Data, San Francisco, CA, USA, 2016.
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@inproceedings{GurajadaSIGMOD2016, TITLE = {Distributed Set Reachability}, AUTHOR = {Gurajada, Sairam and Theobald, Martin}, LANGUAGE = {eng}, ISBN = {978-1-4503-3531-7}, DOI = {10.1145/2882903.2915226}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {SIGMOD'16, ACM SIGMOD International Conference on Management of Data}, PAGES = {1247--1261}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Gurajada, Sairam %A Theobald, Martin %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Distributed Set Reachability : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-220F-5 %R 10.1145/2882903.2915226 %D 2016 %B ACM SIGMOD International Conference on Management of Data %Z date of event: 2016-06-26 - 2016-07-01 %C San Francisco, CA, USA %B SIGMOD'16 %P 1247 - 1261 %I ACM %@ 978-1-4503-3531-7
[134]
M. Halbe, “Skim: Alternative Candidate Selections for Slim through Sketching,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{HalbeBcS2016, TITLE = {Skim: Alternative Candidate Selections for Slim through Sketching}, AUTHOR = {Halbe, Magnus}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, TYPE = {Bachelor's thesis}, }
Endnote
%0 Thesis %A Halbe, Magnus %Y Vreeken, Jilles %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Skim: Alternative Candidate Selections for Slim through Sketching : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5F44-6 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P X, 52 p. %V bachelor %9 bachelor
[135]
J. Hoffart, D. Milchevski, G. Weikum, A. Anand, and J. Singh, “The Knowledge Awakens: Keeping Knowledge Bases Fresh with Emerging Entities,” in WWW’16 Companion, Montréal, Canada, 2016.
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@inproceedings{HoffartWWW2016, TITLE = {The Knowledge Awakens: {K}eeping Knowledge Bases Fresh with Emerging Entities}, AUTHOR = {Hoffart, Johannes and Milchevski, Dragan and Weikum, Gerhard and Anand, Avishek and Singh, Jaspreet}, LANGUAGE = {eng}, ISBN = {978-1-4503-4144-8}, DOI = {10.1145/2872518.2890537}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WWW'16 Companion}, PAGES = {203--206}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Hoffart, Johannes %A Milchevski, Dragan %A Weikum, Gerhard %A Anand, Avishek %A Singh, Jaspreet %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T The Knowledge Awakens: Keeping Knowledge Bases Fresh with Emerging Entities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-01BB-4 %R 10.1145/2872518.2890537 %D 2016 %B 25th International Conference on World Wide Web %Z date of event: 2016-05-11 - 2016-05-15 %C Montréal, Canada %B WWW'16 Companion %P 203 - 206 %I ACM %@ 978-1-4503-4144-8
[136]
K. Hui and K. Berberich, “Cluster Hypothesis in Low-Cost IR Evaluation with Different Document Representations,” in WWW’16 Companion, Montréal, Canada, 2016.
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@inproceedings{HuiWWW2016, TITLE = {Cluster Hypothesis in Low-Cost {IR} Evaluation with Different Document Representations}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-4144-8}, DOI = {10.1145/2872518.2889370}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WWW'16 Companion}, PAGES = {47--48}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Cluster Hypothesis in Low-Cost IR Evaluation with Different Document Representations : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-08E3-C %R 10.1145/2872518.2889370 %D 2016 %B 25th International Conference on World Wide Web %Z date of event: 2016-05-11 - 2016-05-15 %C Montréal, Canada %B WWW'16 Companion %P 47 - 48 %I ACM %@ 978-1-4503-4144-8
[137]
Y. Ibrahim, M. Riedewald, and G. Weikum, “Making Sense of Entities and Quantities in Web Tables,” in CIKM’16, 25th ACM Conference on Information and Knowledge Management, Indianapolis, IN, USA, 2016.
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@inproceedings{Ibrahim:CIKM2016, TITLE = {Making Sense of Entities and Quantities in {Web} Tables}, AUTHOR = {Ibrahim, Yusra and Riedewald, Mirek and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4073-1}, DOI = {10.1145/2983323.2983772}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {CIKM'16, 25th ACM Conference on Information and Knowledge Management}, PAGES = {1703--1712}, ADDRESS = {Indianapolis, IN, USA}, }
Endnote
%0 Conference Proceedings %A Ibrahim, Yusra %A Riedewald, Mirek %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Making Sense of Entities and Quantities in Web Tables : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4852-E %R 10.1145/2983323.2983772 %D 2016 %B 25th ACM Conference on Information and Knowledge Management %Z date of event: 2016-10-24 - 2016-10-28 %C Indianapolis, IN, USA %B CIKM'16 %P 1703 - 1712 %I ACM %@ 978-1-4503-4073-1
[138]
J. Kalofolias, “Maximum Entropy Models for Redescription Mining,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{KalofoliasMSc2016, TITLE = {Maximum Entropy Models for Redescription Mining}, AUTHOR = {Kalofolias, Janis}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Kalofolias, Janis %Y Miettinen, Pauli %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Maximum Entropy Models for Redescription Mining : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-54C0-6 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P III, 51 p. %V master %9 master
[139]
S. Karaev and P. Miettinen, “Cancer: Another Algorithm for Subtropical Matrix Factorization,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016), Riva del Garda, Italy, 2016. (Data Mining and Knowledge Discovery Best Student Paper Award)
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@inproceedings{KaraevECML2016, TITLE = {Cancer: {A}nother Algorithm for Subtropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-3-319-46226-4}, DOI = {10.1007/978-3-319-46227-1_36}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016)}, EDITOR = {Frasconi, Paolo and Landwehr, Niels and Manco, Guiseppe and Vreeken, Jilles}, PAGES = {576--592}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {9852}, ADDRESS = {Riva del Garda, Italy}, }
Endnote
%0 Conference Proceedings %A Karaev, Sanjar %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Cancer: Another Algorithm for Subtropical Matrix Factorization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A926-A %R 10.1007/978-3-319-46227-1_36 %D 2016 %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases %Z date of event: 2016-09-19 - 2016-09-23 %C Riva del Garda, Italy %B Machine Learning and Knowledge Discovery in Databases %E Frasconi, Paolo; Landwehr, Niels; Manco, Guiseppe; Vreeken, Jilles %P 576 - 592 %I Springer %@ 978-3-319-46226-4 %B Lecture Notes in Artificial Intelligence %N 9852
[140]
S. Karaev and P. Miettinen, “Capricorn: An Algorithm for Subtropical Matrix Factorization,” in Proceedings of the Sixteenth SIAM International Conference on Data Mining (SDM 2016), Miama, FL, USA, 2016.
Abstract
Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.
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@inproceedings{karaev16capricorn, TITLE = {Capricorn: {An} Algorithm for Subtropical Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-61197-434-8}, DOI = {10.1137/1.9781611974348.79}, PUBLISHER = {SIAM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, ABSTRACT = {Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.}, BOOKTITLE = {Proceedings of the Sixteenth SIAM International Conference on Data Mining (SDM 2016)}, EDITOR = {Chawla Venkatasubramanian, Sanjay and Meira, Wagner}, PAGES = {702--710}, ADDRESS = {Miama, FL, USA}, }
Endnote
%0 Conference Proceedings %A Karaev, Sanjar %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Capricorn: An Algorithm for Subtropical Matrix Factorization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-542F-3 %R 10.1137/1.9781611974348.79 %D 2016 %B 16th SIAM International Conference on Data Mining %Z date of event: 2016-05-05 - 2016-05-07 %C Miama, FL, USA %X Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data. %B Proceedings of the Sixteenth SIAM International Conference on Data Mining %E Chawla Venkatasubramanian, Sanjay; Meira, Wagner %P 702 - 710 %I SIAM %@ 978-1-61197-434-8
[141]
M. Krötzsch and G. Weikum, “Editorial,” Journal of Web Semantics, vol. 37/38, 2016.
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@article{Kroetzsch2016, TITLE = {Editorial}, AUTHOR = {Kr{\"o}tzsch, Markus and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {1570-8268}, DOI = {10.1016/j.websem.2016.04.002}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, JOURNAL = {Journal of Web Semantics}, VOLUME = {37/38}, PAGES = {53--54}, }
Endnote
%0 Journal Article %A Krötzsch, Markus %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Editorial : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-EB8D-B %R 10.1016/j.websem.2016.04.002 %7 2016 %D 2016 %J Journal of Web Semantics %O Science, Services and Agents on the World Wide Web Web Semantics: Science, Services and Agents on the World Wide Web %V 37/38 %& 53 %P 53 - 54 %I Elsevier %C Amsterdam %@ false
[142]
E. Kuzey, J. Strötgen, V. Setty, and G. Weikum, “Temponym Tagging: Temporal Scopes for Textual Phrases,” in WWW’16 Companion, Montréal, Canada, 2016.
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@inproceedings{Kuzey:2016:TTT:2872518.2889289, TITLE = {Temponym Tagging: {T}emporal Scopes for Textual Phrases}, AUTHOR = {Kuzey, Erdal and Str{\"o}tgen, Jannik and Setty, Vinay and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4144-8}, DOI = {10.1145/2872518.2889289}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WWW'16 Companion}, PAGES = {841--842}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Kuzey, Erdal %A Strötgen, Jannik %A Setty, Vinay %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Temponym Tagging: Temporal Scopes for Textual Phrases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-4134-1 %R 10.1145/2872518.2889289 %D 2016 %B 25th International Conference on World Wide Web %Z date of event: 2016-05-11 - 2016-05-15 %C Montréal, Canada %B WWW'16 Companion %P 841 - 842 %I ACM %@ 978-1-4503-4144-8
[143]
E. Kuzey, V. Setty, J. Strötgen, and G. Weikum, “As Time Goes By: Comprehensive Tagging of Textual Phrases with Temporal Scopes,” in WWW’16, 25th International Conference on World Wide Web, Montréal, Canada, 2016.
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@inproceedings{Kuzey_WWW2016, TITLE = {As Time Goes By: {C}omprehensive Tagging of Textual Phrases with Temporal Scopes}, AUTHOR = {Kuzey, Erdal and Setty, Vinay and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4143-1}, DOI = {10.1145/2872427.2883055}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WWW'16, 25th International Conference on World Wide Web}, PAGES = {915--925}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Kuzey, Erdal %A Setty, Vinay %A Strötgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T As Time Goes By: Comprehensive Tagging of Textual Phrases with Temporal Scopes : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-310D-D %R 10.1145/2872427.2883055 %D 2016 %B 25th International Conference on World Wide Web %Z date of event: 2016-05-11 - 2016-05-15 %C Montréal, Canada %B WWW'16 %P 915 - 925 %I ACM %@ 978-1-4503-4143-1
[144]
S. Metzler, S. Günnemann, and P. Miettinen, “Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques,” 2016. [Online]. Available: http://arxiv.org/abs/1602.04650. (arXiv: 1602.04650)
Abstract
Cliques (or quasi-cliques) are frequently used to model communities: a set of nodes where each pair is (equally) likely to be connected. However, when observing real-world communities, we see that most communities have more structure than that. In particular, the nodes can be ordered in such a way that (almost) all edges in the community lie below a hyperbola. In this paper we present three new models for communities that capture this phenomenon. Our models explain the structure of the communities differently, but we also prove that they are identical in their expressive power. Our models fit to real-world data much better than traditional block models, and allow for more in-depth understanding of the structure of the data.
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@online{Metzler_arXiv2016, TITLE = {Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques}, AUTHOR = {Metzler, Saskia and G{\"u}nnemann, Stephan and Miettinen, Pauli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1602.04650}, EPRINT = {1602.04650}, EPRINTTYPE = {arXiv}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Cliques (or quasi-cliques) are frequently used to model communities: a set of nodes where each pair is (equally) likely to be connected. However, when observing real-world communities, we see that most communities have more structure than that. In particular, the nodes can be ordered in such a way that (almost) all edges in the community lie below a hyperbola. In this paper we present three new models for communities that capture this phenomenon. Our models explain the structure of the communities differently, but we also prove that they are identical in their expressive power. Our models fit to real-world data much better than traditional block models, and allow for more in-depth understanding of the structure of the data.}, }
Endnote
%0 Report %A Metzler, Saskia %A Günnemann, Stephan %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-08E5-8 %U http://arxiv.org/abs/1602.04650 %D 2016 %X Cliques (or quasi-cliques) are frequently used to model communities: a set of nodes where each pair is (equally) likely to be connected. However, when observing real-world communities, we see that most communities have more structure than that. In particular, the nodes can be ordered in such a way that (almost) all edges in the community lie below a hyperbola. In this paper we present three new models for communities that capture this phenomenon. Our models explain the structure of the communities differently, but we also prove that they are identical in their expressive power. Our models fit to real-world data much better than traditional block models, and allow for more in-depth understanding of the structure of the data. %K cs.SI, Physics, Physics and Society, physics.soc-ph
[145]
P. Mirza, S. Razniewski, and W. Nutt, “Expanding Wikidata’s Parenthood Information by 178%, or How To Mine Relation Cardinalities,” in Proceedings of the ISWC 2016 Posters & Demonstrations Track co-located with 15th International Semantic Web Conference (ISWC-P&D 2016), Kobe, Japan, 2016.
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@inproceedings{DBLP:conf/semweb/MirzaRN16, TITLE = {Expanding {W}ikidata's Parenthood Information by 178{\%}, or How To Mine Relation Cardinalities}, AUTHOR = {Mirza, Paramita and Razniewski, Simon and Nutt, Werner}, LANGUAGE = {eng}, URL = {urn:nbn:de:0074-1690-5}, PUBLISHER = {CEUR-WS.org}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the ISWC 2016 Posters \& Demonstrations Track co-located with 15th International Semantic Web Conference (ISWC-P\&D 2016)}, EDITOR = {Kawamura, Takahiro and Paulheim, Heiko}, EID = {4}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {1690}, ADDRESS = {Kobe, Japan}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Razniewski, Simon %A Nutt, Werner %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Expanding Wikidata's Parenthood Information by 178%, or How To Mine Relation Cardinalities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-23C1-9 %D 2016 %B ISWC 2016 Posters & Demonstrations Trac %Z date of event: 2016-10-19 - 2016-10-19 %C Kobe, Japan %B Proceedings of the ISWC 2016 Posters & Demonstrations Track co-located with 15th International Semantic Web Conference %E Kawamura, Takahiro; Paulheim, Heiko %Z sequence number: 4 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 1690
[146]
P. Mirza and S. Tonelli, “CATENA: CAusal and TEmporal relation extraction from NAtural language texts,” in Proceedings of COLING 2016: Technical Papers, Osaka, Japan, 2016.
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@inproceedings{mirza-tonelli:2016:COLING1, TITLE = {{CATENA}: {CAusal} and {TEmporal} relation extraction from {NAtural} language texts}, AUTHOR = {Mirza, Paramita and Tonelli, Sara}, LANGUAGE = {eng}, ISBN = {978-4-87974-702-0}, PUBLISHER = {ACL}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of COLING 2016: Technical Papers}, PAGES = {64--75}, ADDRESS = {Osaka, Japan}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Tonelli, Sara %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T CATENA: CAusal and TEmporal relation extraction from NAtural language texts : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-23B8-0 %D 2016 %B The 26th International Conference on Computational Linguistics %Z date of event: 2016-12-11 - 2016-12-16 %C Osaka, Japan %B Proceedings of COLING 2016: Technical Papers %P 64 - 75 %I ACL %@ 978-4-87974-702-0
[147]
P. Mirza and S. Tonelli, “On the Contribution of Word Embeddings to Temporal Relation Classification,” in Proceedings of COLING 2016: Technical Papers, Osaka, Japan, 2016.
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@inproceedings{mirza-tonelli:2016:COLING2, TITLE = {On the Contribution of Word Embeddings to Temporal Relation Classification}, AUTHOR = {Mirza, Paramita and Tonelli, Sara}, LANGUAGE = {eng}, ISBN = {978-4-87974-702-0}, PUBLISHER = {ACL}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of COLING 2016: Technical Papers}, PAGES = {2818--2828}, ADDRESS = {Osaka, Japan}, }
Endnote
%0 Conference Proceedings %A Mirza, Paramita %A Tonelli, Sara %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T On the Contribution of Word Embeddings to Temporal Relation Classification : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-23BB-A %D 2016 %B The 26th International Conference on Computational Linguistics %Z date of event: 2016-12-11 - 2016-12-16 %C Osaka, Japan %B Proceedings of COLING 2016: Technical Papers %P 2818 - 2828 %I ACL %@ 978-4-87974-702-0
[148]
A. Mishra and K. Berberich, “Leveraging Semantic Annotations to Link Wikipedia and News Archives,” in Advances in Information Retrieval (ECIR 2016), Padova, Italy, 2016.
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@inproceedings{MishraECIR2016, TITLE = {Leveraging Semantic Annotations to Link {W}ikipedia and News Archives}, AUTHOR = {Mishra, Arunav and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-30670-4}, DOI = {10.1007/978-3-319-30671-1_3}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2016)}, EDITOR = {Ferro, Nicola and Crestani, Fabio and Moens, Marie-Francine and Mothe, Josiane and Silvestre, Fabrizio and Di Nunzio, Giorgio Maria and Hauff, Claudia and Silvello, Gianmaria}, PAGES = {30--42}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9626}, ADDRESS = {Padova, Italy}, }
Endnote
%0 Conference Proceedings %A Mishra, Arunav %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Leveraging Semantic Annotations to Link Wikipedia and News Archives : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-48DC-F %R 10.1007/978-3-319-30671-1_3 %D 2016 %B 38th European Conference on Information Retrieval %Z date of event: 2016-03-20 - 2016-03-23 %C Padova, Italy %B Advances in Information Retrieval %E Ferro, Nicola; Crestani, Fabio; Moens, Marie-Francine; Mothe, Josiane; Silvestre, Fabrizio; Di Nunzio, Giorgio Maria; Hauff, Claudia; Silvello, Gianmaria %P 30 - 42 %I Springer %@ 978-3-319-30670-4 %B Lecture Notes in Computer Science %N 9626
[149]
A. Mishra and K. Berberich, “Estimating Time Models for News Article Excerpts,” in CIKM’16, 25th ACM Conference on Information and Knowledge Management, Indianapolis, IN, USA, 2016.
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@inproceedings{DBLP:conf/cikm/MishraB16, TITLE = {Estimating Time Models for News Article Excerpts}, AUTHOR = {Mishra, Arunav and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-4073-1}, DOI = {10.1145/2983323.2983802}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {CIKM'16, 25th ACM Conference on Information and Knowledge Management}, PAGES = {781--790}, ADDRESS = {Indianapolis, IN, USA}, }
Endnote
%0 Conference Proceedings %A Mishra, Arunav %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Estimating Time Models for News Article Excerpts : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-20CF-3 %R 10.1145/2983323.2983802 %D 2016 %B 25th ACM Conference on Information and Knowledge Management %Z date of event: 2016-10-24 - 2016-10-28 %C Indianapolis, IN, USA %B CIKM'16 %P 781 - 790 %I ACM %@ 978-1-4503-4073-1
[150]
A. Mishra and K. Berberich, “Event Digest: A Holistic View on Past Events,” in SIGIR’16, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, 2016.
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@inproceedings{MishraSIGIR2016, TITLE = {Event Digest: {A} Holistic View on Past Events}, AUTHOR = {Mishra, Arunav and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-4069-4}, DOI = {10.1145/2911451.2911526}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {SIGIR'16, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {493--502}, ADDRESS = {Pisa, Italy}, }
Endnote
%0 Conference Proceedings %A Mishra, Arunav %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Event Digest: A Holistic View on Past Events : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-0895-D %R 10.1145/2911451.2911526 %D 2016 %B 39th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2016-07-17 - 2016-07-21 %C Pisa, Italy %B SIGIR'16 %P 493 - 502 %I ACM %@ 978-1-4503-4069-4
[151]
S. Mukherjee, S. Günnemann, and G. Weikum, “Continuous Experience-aware Language Model,” in KDD’16, 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016.
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@inproceedings{MukherjeeKDD2016, TITLE = {Continuous Experience-aware Language Model}, AUTHOR = {Mukherjee, Subhabrata and G{\"u}nnemann, Stephan and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4232-2}, DOI = {10.1145/2939672.2939780}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {KDD'16, 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, PAGES = {1075--1084}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Mukherjee, Subhabrata %A Günnemann, Stephan %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Continuous Experience-aware Language Model : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A678-6 %R 10.1145/2939672.2939780 %D 2016 %B 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining %Z date of event: 2016-08-13 - 2016-08-17 %C San Francisco, CA, USA %B KDD'16 %P 1075 - 1084 %I ACM %@ 978-1-4503-4232-2
[152]
S. Mukherjee, S. Dutta, and G. Weikum, “Credible Review Detection with Limited Information Using Consistency Features,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016), Riva del Garda, Italy, 2016.
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@inproceedings{MukherjeeECML2016, TITLE = {Credible Review Detection with Limited Information Using Consistency Features}, AUTHOR = {Mukherjee, Subhabrata and Dutta, Sourav and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-319-46226-4}, DOI = {10.1007/978-3-319-46227-1_13}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016)}, EDITOR = {Frasconi, Paolo and Landwehr, Niels and Manco, Guiseppe and Vreeken, Jilles}, PAGES = {195--213}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {9852}, ADDRESS = {Riva del Garda, Italy}, }
Endnote
%0 Conference Proceedings %A Mukherjee, Subhabrata %A Dutta, Sourav %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Credible Review Detection with Limited Information Using Consistency Features : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A67C-D %R 10.1007/978-3-319-46227-1_13 %D 2016 %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases %Z date of event: 2016-09-19 - 2016-09-23 %C Riva del Garda, Italy %B Machine Learning and Knowledge Discovery in Databases %E Frasconi, Paolo; Landwehr, Niels; Manco, Guiseppe; Vreeken, Jilles %P 195 - 213 %I Springer %@ 978-3-319-46226-4 %B Lecture Notes in Artificial Intelligence %N 9852
[153]
N. Mukuze and P. Miettinen, “Interactive Constrained Boolean Matrix Factorization,” in Proceedings of the ACM SIGKDD 2016 Full-day Workshop on Interactive Data Exploration and Analytics (IDEA 2016), San Francisco, CA, USA, 2016.
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@inproceedings{mukuze16interactive, TITLE = {Interactive Constrained {B}oolean Matrix Factorization}, AUTHOR = {Mukuze, Nelson and Miettinen, Pauli}, LANGUAGE = {eng}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the ACM SIGKDD 2016 Full-day Workshop on Interactive Data Exploration and Analytics (IDEA 2016)}, EDITOR = {Chau, Duen Horng and Vreeken, Jilles and van Leeuwen, Matthijs and Shahaf, Dafna and Faloutsos, Christos}, PAGES = {96--104}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Mukuze, Nelson %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Interactive Constrained Boolean Matrix Factorization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-226C-2 %D 2016 %B ACM SIGKDD 2016 Full-day Workshop on Interactive Data Exploration and Analytics %Z date of event: 2015-08-14 - 2014-08-14 %C San Francisco, CA, USA %B Proceedings of the ACM SIGKDD 2016 Full-day Workshop on Interactive Data Exploration and Analytics %E Chau, Duen Horng; Vreeken, Jilles; van Leeuwen, Matthijs; Shahaf, Dafna; Faloutsos , Christos %P 96 - 104
[154]
N. Mukuze, “Interactive Boolean Matrix Factorization,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{MukuzeMSc2016, TITLE = {Interactive Boolean Matrix Factorization}, AUTHOR = {Mukuze, Nelson}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Mukuze, Nelson %Y Miettinen, Pauli %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Interactive Boolean Matrix Factorization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-54C8-5 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P III, 68 p. %V master %9 master
[155]
S. Nag Chowdhury, “Commonsense for Making Sense of Data,” in Proceedings of the VLDB 2016 PhD Workshop co-located with the 42nd International Conference on Very Large Databases (VLDB 2016), New Delhi, India, 2016.
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@inproceedings{NagChowdhuryVLDB2016, TITLE = {Commonsense for Making Sense of Data}, AUTHOR = {Nag Chowdhury, Sreyasi}, LANGUAGE = {eng}, URL = {urn:nbn:de:0074-1671-7; urn:nbn:de:0074-1671-7}, PUBLISHER = {CEUR-WS.org}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the VLDB 2016 PhD Workshop co-located with the 42nd International Conference on Very Large Databases (VLDB 2016)}, EDITOR = {Grust, Torsten and Karlapalem, Kamal and Pavlo, Andyq}, EID = {8}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {1671}, ADDRESS = {New Delhi, India}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Commonsense for Making Sense of Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-22E4-3 %U urn:nbn:de:0074-1671-7 %D 2016 %B VLDB 2016 PhD Workshop %Z date of event: 2016-09-09 - 2016-09-09 %C New Delhi, India %B Proceedings of the VLDB 2016 PhD Workshop co-located with the 42nd International Conference on Very Large Databases (VLDB 2016) %E Grust, Torsten; Karlapalem, Kamal; Pavlo, Andyq %Z sequence number: 8 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 1671
[156]
S. Nag Chowdhury, N. Tandon, and G. Weikum, “Know2Look: Commonsense Knowledge for Visual Search,” in AKBC 2016, 5th Workshop on Automated Knowledge Base Construction, San Diego, CA, USA, 2016.
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@inproceedings{DBLP:conf/akbc/ChowdhuryTW16, TITLE = {{Know2Look}: {C}ommonsense Knowledge for Visual Search}, AUTHOR = {Nag Chowdhury, Sreyasi and Tandon, Niket and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://www.akbc.ws/2016/papers/11_Paper.pdf}, PUBLISHER = {AKBC Board}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {AKBC 2016, 5th Workshop on Automated Knowledge Base Construction}, PAGES = {57--62}, ADDRESS = {San Diego, CA, USA}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %A Tandon, Niket %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Know2Look: Commonsense Knowledge for Visual Search : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A633-2 %U http://www.akbc.ws/2016/papers/11_Paper.pdf %D 2016 %B 5th Workshop on Automated Knowledge Base Construction %Z date of event: 2016-06-17 - 2016-06-17 %C San Diego, CA, USA %B AKBC 2016 %P 57 - 62 %I AKBC Board
[157]
D. B. Nguyen, M. Theobald, and G. Weikum, “J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features,” Transactions of the Association for Computational Linguistics, vol. 4, 2016.
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@article{Nguyen2016, TITLE = {{J}-{NERD}: {J}oint {N}amed {E}ntity {R}ecognition and {D}isambiguation with Rich Linguistic Features}, AUTHOR = {Nguyen, Dat Ba and Theobald, Martin and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {2307-387X}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, JOURNAL = {Transactions of the Association for Computational Linguistics}, VOLUME = {4}, PAGES = {215--229}, }
Endnote
%0 Journal Article %A Nguyen, Dat Ba %A Theobald, Martin %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-0199-1 %7 2016 %D 2016 %J Transactions of the Association for Computational Linguistics %O TACL %V 4 %& 215 %P 215 - 229 %@ false %U https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/698
[158]
D. B. Nguyen, A. Abujabal, N. K. Tran, M. Theobald, and G. Weikum, “Query-Driven On-The-Fly Knowledge Base Construction,” Proceedings of the VLDB Endowment (Proc. VLDB 2017), vol. 9, no. 1, 2016.
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@article{escidoc:2530450, TITLE = {Query-Driven On-The-Fly Knowledge Base Construction}, AUTHOR = {Nguyen, Dat Ba and Abujabal, Abdalghani and Tran, Nam Khanh and Theobald, Martin and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.14778/3136610.3136616}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, JOURNAL = {Proceedings of the VLDB Endowment (Proc. VLDB)}, VOLUME = {9}, NUMBER = {1}, PAGES = {66--79}, BOOKTITLE = {Proceedings of the 44th International Conference on Very Large Data Bases (VLDB 2017)}, EDITOR = {Bhowmick, Sourav and Torres, Ricardo}, }
Endnote
%0 Journal Article %A Nguyen, Dat Ba %A Abujabal, Abdalghani %A Tran, Nam Khanh %A Theobald, Martin %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Query-Driven On-The-Fly Knowledge Base Construction : %G eng %U http://hdl.handle.net/21.11116/0000-0000-3B51-3 %R 10.14778/3136610.3136616 %7 2016 %D 2016 %J Proceedings of the VLDB Endowment %O PVLDB %V 9 %N 1 %& 66 %P 66 - 79 %I ACM %C New York, NY %B Proceedings of the 44th International Conference on Very Large Data Bases %O VLDB 2017 Rio de Janeiro, Brazil, August 27-31, 2018
[159]
H.-V. Nguyen and J. Vreeken, “Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series,” in Proceedings of the Sixteenth SIAM International Conference on Data Mining (SDM 2016), Miama, FL, USA, 2016.
Abstract
Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.
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@inproceedings{VreekenSDM2016, TITLE = {Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series}, AUTHOR = {Nguyen, Hoang-Vu and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-434-8}, DOI = {10.1137/1.9781611974348.93}, PUBLISHER = {SIAM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, ABSTRACT = {Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.}, BOOKTITLE = {Proceedings of the Sixteenth SIAM International Conference on Data Mining (SDM 2016)}, EDITOR = {Chawla Venkatasubramanian, Sanjay and Meira, Wagner}, PAGES = {828--836}, ADDRESS = {Miama, FL, USA}, }
Endnote
%0 Conference Proceedings %A Nguyen, Hoang-Vu %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A937-4 %R 10.1137/1.9781611974348.93 %D 2016 %B 16th SIAM International Conference on Data Mining %Z date of event: 2016-05-05 - 2016-05-07 %C Miama, FL, USA %X Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data. %B Proceedings of the Sixteenth SIAM International Conference on Data Mining %E Chawla Venkatasubramanian, Sanjay; Meira, Wagner %P 828 - 836 %I SIAM %@ 978-1-61197-434-8
[160]
H.-V. Nguyen and J. Vreeken, “Flexibly Mining Better Subgroups,” in Proceedings of the Sixteenth SIAM International Conference on Data Mining (SDM 2016), Miama, FL, USA, 2016.
Abstract
Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.
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@inproceedings{NguyenSDM2016, TITLE = {Flexibly Mining Better Subgroups}, AUTHOR = {Nguyen, Hoang-Vu and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-434-8}, DOI = {10.1137/1.9781611974348.66}, PUBLISHER = {SIAM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, ABSTRACT = {Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.}, BOOKTITLE = {Proceedings of the Sixteenth SIAM International Conference on Data Mining (SDM 2016)}, EDITOR = {Chawla Venkatasubramanian, Sanjay and Meira, Wagner}, PAGES = {585--593}, ADDRESS = {Miama, FL, USA}, }
Endnote
%0 Conference Proceedings %A Nguyen, Hoang-Vu %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Flexibly Mining Better Subgroups : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A933-C %R 10.1137/1.9781611974348.66 %D 2016 %B 16th SIAM International Conference on Data Mining %Z date of event: 2016-05-05 - 2016-05-07 %C Miama, FL, USA %X Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data. %B Proceedings of the Sixteenth SIAM International Conference on Data Mining %E Chawla Venkatasubramanian, Sanjay; Meira, Wagner %P 585 - 593 %I SIAM %@ 978-1-61197-434-8
[161]
H.-V. Nguyen, P. Mandros, and J. Vreeken, “Universal Dependency Analysis,” in Proceedings of the Sixteenth SIAM International Conference on Data Mining (SDM 2016), Miama, FL, USA, 2016.
Abstract
Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.
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@inproceedings{MandrosSDM2016, TITLE = {Universal Dependency Analysis}, AUTHOR = {Nguyen, Hoang-Vu and Mandros, Panagiotis and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-434-8}, DOI = {10.1137/1.9781611974348.89}, PUBLISHER = {SIAM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, ABSTRACT = {Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.}, BOOKTITLE = {Proceedings of the Sixteenth SIAM International Conference on Data Mining (SDM 2016)}, EDITOR = {Chawla Venkatasubramanian, Sanjay and Meira, Wagner}, PAGES = {792--800}, ADDRESS = {Miama, FL, USA}, }
Endnote
%0 Conference Proceedings %A Nguyen, Hoang-Vu %A Mandros, Panagiotis %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Universal Dependency Analysis : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A935-8 %R 10.1137/1.9781611974348.89 %D 2016 %B 16th SIAM International Conference on Data Mining %Z date of event: 2016-05-05 - 2016-05-07 %C Miama, FL, USA %X Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data. %B Proceedings of the Sixteenth SIAM International Conference on Data Mining %E Chawla Venkatasubramanian, Sanjay; Meira, Wagner %P 792 - 800 %I SIAM %@ 978-1-61197-434-8
[162]
K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum, “Credibility Assessment of Textual Claims on the Web,” in CIKM’16, 25th ACM International Conference on Information and Knowledge Management, Indianapolis, IN, USA, 2016.
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@inproceedings{PopatCIKM2016, TITLE = {Credibility Assessment of Textual Claims on the {Web}}, AUTHOR = {Popat, Kashyap and Mukherjee, Subhabrata and Str{\"o}tgen, Jannik and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4073-1}, DOI = {10.1145/2983323.2983661}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {CIKM'16, 25th ACM International Conference on Information and Knowledge Management}, PAGES = {2173--2178}, ADDRESS = {Indianapolis, IN, USA}, }
Endnote
%0 Conference Proceedings %A Popat, Kashyap %A Mukherjee, Subhabrata %A Strötgen, Jannik %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Credibility Assessment of Textual Claims on the Web : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-B260-3 %R 10.1145/2983323.2983661 %D 2016 %B 25th ACM International Conference on Information and Knowledge Management %Z date of event: 2016-10-24 - 2016-10-28 %C Indianapolis, IN, USA %B CIKM'16 %P 2173 - 2178 %I ACM %@ 978-1-4503-4073-1
[163]
T. Rebele, F. Suchanek, J. Hoffart, J. Biega, E. Kuzey, and G. Weikum, “YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames,” in The Semantic Web -- ISWC 2016, Kobe, Japan, 2016.
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@inproceedings{RebeleISWC2016, TITLE = {{YAGO}: A Multilingual Knowledge Base from {W}ikipedia, {W}ordnet, and {G}eonames}, AUTHOR = {Rebele, Thomas and Suchanek, Fabian and Hoffart, Johannes and Biega, Joanna and Kuzey, Erdal and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-319-46546-3}, DOI = {10.1007/978-3-319-46547-0_19}, PUBLISHER = {Springer}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {The Semantic Web -- ISWC 2016}, EDITOR = {Groth, Paul and Simperl, Elena and Gray, Alasdair and Sabou, Marta and Kr{\"o}tzsch, Markus and Lecue, Freddy and Fl{\"o}ck, Fabian and Gil, Yolanda}, PAGES = {177--185}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9982}, ADDRESS = {Kobe, Japan}, }
Endnote
%0 Conference Proceedings %A Rebele, Thomas %A Suchanek, Fabian %A Hoffart, Johannes %A Biega, Joanna %A Kuzey, Erdal %A Weikum, Gerhard %+ Télécom ParisTech Télécom ParisTech Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A69A-9 %R 10.1007/978-3-319-46547-0_19 %D 2016 %B 15th International Semantic Web Conference %Z date of event: 2016-10-17 - 2016-10-21 %C Kobe, Japan %B The Semantic Web -- ISWC 2016 %E Groth, Paul; Simperl, Elena; Gray, Alasdair; Sabou, Marta; Krötzsch, Markus; Lecue, Freddy; Flöck, Fabian; Gil, Yolanda %P 177 - 185 %I Springer %@ 978-3-319-46546-3 %B Lecture Notes in Computer Science %N 9982
[164]
M. Salyaeva, “Summarising and Recommending with Skipisodes,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{SalyaevaMSc2016, TITLE = {Summarising and Recommending with Skipisodes}, AUTHOR = {Salyaeva, Margarita}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Salyaeva, Margarita %Y Vreeken, Jilles %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Summarising and Recommending with Skipisodes : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5F46-2 %I Universität des Saarlandes %C Saarbrücken %D 2016 %V master %9 master
[165]
A. Schmidt, J. Hoffart, D. Milchevski, and G. Weikum, “Context-Sensitive Auto-Completion for Searching with Entities and Categories,” in SIGIR’16, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, 2016.
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@inproceedings{SchmidtIGIR2016, TITLE = {Context-Sensitive Auto-Completion for Searching with Entities and Categories}, AUTHOR = {Schmidt, Andreas and Hoffart, Johannes and Milchevski, Dragan and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4069-4}, DOI = {10.1145/2911451.2911461}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {SIGIR'16, 39th International ACM SIGIR Conference on Research and Development in Information Retrieval}, PAGES = {1097--1100}, ADDRESS = {Pisa, Italy}, }
Endnote
%0 Conference Proceedings %A Schmidt, Andreas %A Hoffart, Johannes %A Milchevski, Dragan %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Context-Sensitive Auto-Completion for Searching with Entities and Categories : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A924-E %R 10.1145/2911451.2911461 %D 2016 %B 39th International ACM SIGIR Conference on Research and Development in Information Retrieval %Z date of event: 2016-07-17 - 2016-07-21 %C Pisa, Italy %B SIGIR'16 %P 1097 - 1100 %I ACM %@ 978-1-4503-4069-4
[166]
S. Seufert, P. Ernst, S. J. Bedathur, S. K. Kondreddi, K. Berberich, and G. Weikum, “Instant Espresso: Interactive Analysis of Relationships in Knowledge Graphs,” in WWW’16 Companion, Montréal, Canada, 2016.
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@inproceedings{SeufertWWW2016, TITLE = {Instant {E}spresso: {I}nteractive Analysis of Relationships in Knowledge Graphs}, AUTHOR = {Seufert, Stephan and Ernst, Patrick and Bedathur, Srikanta J. and Kondreddi, Sarath Kumar and Berberich, Klaus and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4144-8}, DOI = {10.1145/2872518.2890528}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WWW'16 Companion}, PAGES = {251--254}, ADDRESS = {Montr{\'e}al, Canada}, }
Endnote
%0 Conference Proceedings %A Seufert, Stephan %A Ernst, Patrick %A Bedathur, Srikanta J. %A Kondreddi, Sarath Kumar %A Berberich, Klaus %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Instant Espresso: Interactive Analysis of Relationships in Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-01BD-F %R 10.1145/2872518.2890528 %D 2016 %B 25th International Conference on World Wide Web %Z date of event: 2016-05-11 - 2016-05-15 %C Montréal, Canada %B WWW'16 Companion %P 251 - 254 %I ACM %@ 978-1-4503-4144-8
[167]
S. Seufert, K. Berberich, S. J. Bedathur, S. K. Kondreddi, P. Ernst, and G. Weikum, “ESPRESSO: Explaining Relationships between Entity Sets,” in CIKM’16, 25th ACM Conference on Information and Knowledge Management, Indianapolis, IN, USA, 2016.
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@inproceedings{DBLP:conf/cikm/SeufertBBKEW16, TITLE = {{ESPRESSO}: {E}xplaining Relationships between Entity Sets}, AUTHOR = {Seufert, Stephan and Berberich, Klaus and Bedathur, Srikanta J. and Kondreddi, Sarath Kumar and Ernst, Patrick and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-4073-1}, DOI = {10.1145/2983323.2983778}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {CIKM'16, 25th ACM Conference on Information and Knowledge Management}, PAGES = {1311--1320}, ADDRESS = {Indianapolis, IN, USA}, }
Endnote
%0 Conference Proceedings %A Seufert, Stephan %A Berberich, Klaus %A Bedathur, Srikanta J. %A Kondreddi, Sarath Kumar %A Ernst, Patrick %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T ESPRESSO: Explaining Relationships between Entity Sets : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-20D3-8 %R 10.1145/2983323.2983778 %D 2016 %B 25th ACM Conference on Information and Knowledge Management %Z date of event: 2016-10-24 - 2016-10-28 %C Indianapolis, IN, USA %B CIKM'16 %P 1311 - 1320 %I ACM %@ 978-1-4503-4073-1
[168]
D. Seyler, M. Yahya, K. Berberich, and O. Alonso, “Automated Question Generation for Quality Control in Human Computation Tasks,” in WebSci’16, ACM Web Science Conference, Hannover, Germany, 2016.
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@inproceedings{SeylerWebSci2016, TITLE = {Automated Question Generation for Quality Control in Human Computation Tasks}, AUTHOR = {Seyler, Dominic and Yahya, Mohamed and Berberich, Klaus and Alonso, Omar}, LANGUAGE = {eng}, ISBN = {978-1-4503-4208-7}, DOI = {10.1145/2908131.2908210}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WebSci'16, ACM Web Science Conference}, PAGES = {360--362}, ADDRESS = {Hannover, Germany}, }
Endnote
%0 Conference Proceedings %A Seyler, Dominic %A Yahya, Mohamed %A Berberich, Klaus %A Alonso, Omar %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Automated Question Generation for Quality Control in Human Computation Tasks : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-08DF-7 %R 10.1145/2908131.2908210 %D 2016 %B ACM Web Science Conference %Z date of event: 2016-05-22 - 2016-05-25 %C Hannover, Germany %B WebSci'16 %P 360 - 362 %I ACM %@ 978-1-4503-4208-7
[169]
D. Seyler, M. Yahya, and K. Berberich, “Knowledge Questions from Knowledge Graphs,” 2016. [Online]. Available: http://arxiv.org/abs/1610.09935. (arXiv: 1610.09935)
Abstract
We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose an end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. If a multiple-choice question is desired, the approach selects alternative answer options. Finally, our approach uses a template-based method to verbalize the structured query and yield a natural language question. A key challenge is estimating how difficult the generated question is to human users. To do this, we make use of historical data from the Jeopardy! quiz show and a semantically annotated Web-scale document collection, engineer suitable features, and train a logistic regression classifier to predict question difficulty. Experiments demonstrate the viability of our overall approach.
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@online{Seyler1610.09935, TITLE = {Knowledge Questions from Knowledge Graphs}, AUTHOR = {Seyler, Dominic and Yahya, Mohamed and Berberich, Klaus}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1610.09935}, EPRINT = {1610.09935}, EPRINTTYPE = {arXiv}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, ABSTRACT = {We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose an end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. If a multiple-choice question is desired, the approach selects alternative answer options. Finally, our approach uses a template-based method to verbalize the structured query and yield a natural language question. A key challenge is estimating how difficult the generated question is to human users. To do this, we make use of historical data from the Jeopardy! quiz show and a semantically annotated Web-scale document collection, engineer suitable features, and train a logistic regression classifier to predict question difficulty. Experiments demonstrate the viability of our overall approach.}, }
Endnote
%0 Report %A Seyler, Dominic %A Yahya, Mohamed %A Berberich, Klaus %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowledge Questions from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1CB5-F %U http://arxiv.org/abs/1610.09935 %D 2016 %X We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose an end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. If a multiple-choice question is desired, the approach selects alternative answer options. Finally, our approach uses a template-based method to verbalize the structured query and yield a natural language question. A key challenge is estimating how difficult the generated question is to human users. To do this, we make use of historical data from the Jeopardy! quiz show and a semantically annotated Web-scale document collection, engineer suitable features, and train a logistic regression classifier to predict question difficulty. Experiments demonstrate the viability of our overall approach. %K Computer Science, Computation and Language, cs.CL
[170]
A. Shah, “Recognizing Visual Activities,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{ShahMSc2016, TITLE = {Recognizing Visual Activities}, AUTHOR = {Shah, Ali}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Shah, Ali %Y Weikum, Gerhard %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Recognizing Visual Activities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-439D-1 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P 56 p. %V master %9 master
[171]
J. Singh, J. Hoffart, and A. Anand, “Discovering Entities with Just a Little Help from You,” in CIKM’16, 25th ACM Conference on Information and Knowledge Management, Indianapolis, IN, USA, 2016.
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@inproceedings{Singh:2016:DEJ:2983323.2983798, TITLE = {Discovering Entities with Just a Little Help from You}, AUTHOR = {Singh, Jaspreet and Hoffart, Johannes and Anand, Avishek}, LANGUAGE = {eng}, ISBN = {978-1-4503-4073-1}, DOI = {10.1145/2983323.2983798}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {CIKM'16, 25th ACM Conference on Information and Knowledge Management}, PAGES = {1331--1340}, ADDRESS = {Indianapolis, IN, USA}, }
Endnote
%0 Conference Proceedings %A Singh, Jaspreet %A Hoffart, Johannes %A Anand, Avishek %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Discovering Entities with Just a Little Help from You : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1CC2-2 %R 10.1145/2983323.2983798 %D 2016 %B 25th ACM Conference on Information and Knowledge Management %Z date of event: 2016-10-24 - 2016-10-28 %C Indianapolis, IN, USA %B CIKM'16 %P 1331 - 1340 %I ACM %@ 978-1-4503-4073-1
[172]
A. Siu, P. Ernst, and G. Weikum, “Disambiguation of Entities in MEDLINE Abstracts by Combining MeSH Terms with Knowledge,” in Proceedings of the 15th Workshop on Biomedical Natural Language Processing (BioNLP 2016), Berlin, Germany, 2016.
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@inproceedings{Siu16, TITLE = {Disambiguation of entities in {MEDLINE} abstracts by combining {MeSH} terms with knowledge}, AUTHOR = {Siu, Amy and Ernst, Patrick and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-945626-12-8}, PUBLISHER = {ACL}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Proceedings of the 15th Workshop on Biomedical Natural Language Processing (BioNLP 2016)}, PAGES = {72--76}, ADDRESS = {Berlin, Germany}, }
Endnote
%0 Conference Proceedings %A Siu, Amy %A Ernst, Patrick %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Disambiguation of Entities in MEDLINE Abstracts by Combining MeSH Terms with Knowledge : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2040-3 %D 2016 %B 15th Workshop on Biomedical Natural Language Processing %Z date of event: 2016-08-12 - 2016-08-12 %C Berlin, Germany %B Proceedings of the 15th Workshop on Biomedical Natural Language Processing %P 72 - 76 %I ACL %@ 978-1-945626-12-8 %U http://aclweb.org/anthology/W/W16/W16-2909.pdf
[173]
D. Spanier, “An Incremental Approach to Distilling Named Events from News Streams,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{SpanierMSc2016, TITLE = {An Incremental Approach to Distilling Named Events from News Streams}, AUTHOR = {Spanier, Daniel}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Spanier, Daniel %Y Weikum, Gerhard %A referee: Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T An Incremental Approach to Distilling Named Events from News Streams : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4913-0 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P XI, 58 p. %V master %9 master
[174]
J. Strötgen and M. Gertz, Domain-Sensitive Temporal Tagging. San Rafael, CA: Morgan & Claypool Publishers, 2016.
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@book{StroetgenBook2016, TITLE = {Domain-Sensitive Temporal Tagging}, AUTHOR = {Str{\"o}tgen, Jannik and Gertz, Michael}, LANGUAGE = {eng}, ISSN = {1947-4040}, ISBN = {9781627054591; 9781627054997}, DOI = {10.2200/S00721ED1V01Y201606HLT036}, PUBLISHER = {Morgan \& Claypool Publishers}, ADDRESS = {San Rafael, CA}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, PAGES = {151 p.}, SERIES = {Synthesis Lectures on Human Language Technologies}, }
Endnote
%0 Book %A Strötgen, Jannik %A Gertz, Michael %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Domain-Sensitive Temporal Tagging : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-1777-9 %@ 9781627054591 %@ 9781627054997 %R 10.2200/S00721ED1V01Y201606HLT036 %I Morgan & Claypool Publishers %C San Rafael, CA %D 2016 %P 151 p. %B Synthesis Lectures on Human Language Technologies %@ false
[175]
J. Strötgen, “Domänen-sensitives Temporal Tagging für Event-zentriertes Information Retrieval,” in Ausgezeichnete Informatikdissertationen 2015, Bonn: GI, 2016.
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@incollection{StrotgenLNI_Diss16, TITLE = {{Dom{\"a}nen-sensitives Temporal Tagging f{\"u}r Event-zentriertes Information Retrieval}}, AUTHOR = {Str{\"o}tgen, Jannik}, LANGUAGE = {deu}, ISBN = {978-3-88579-975-7}, PUBLISHER = {GI}, ADDRESS = {Bonn}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Ausgezeichnete Informatikdissertationen 2015}, EDITOR = {H{\"o}lldobler, Steffen}, PAGES = {279--288}, SERIES = {Lecture Notes in Informatics -- Dissertations}, VOLUME = {16}, }
Endnote
%0 Book Section %A Strötgen, Jannik %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Domänen-sensitives Temporal Tagging für Event-zentriertes Information Retrieval : %G deu %U http://hdl.handle.net/11858/00-001M-0000-002B-B26A-F %D 2016 %B Ausgezeichnete Informatikdissertationen 2015 %E Hölldobler, Steffen %P 279 - 288 %I GI %C Bonn %@ 978-3-88579-975-7 %S Lecture Notes in Informatics - Dissertations %N 16
[176]
A. Talaika, J. Biega, A. Amarilli, and F. M. Suchanek, “IBEX: Harvesting Entities from the Web Using Unique Identifiers,” in Proceedings of the 18th International Workshop on Web and Databases (WebDB 2015), Melbourne, Australia, 2016.
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@inproceedings{Talaika2016, TITLE = {{IBEX}: {H}arvesting Entities from the {Web} Using Unique Identifiers}, AUTHOR = {Talaika, Aliaksandr and Biega, Joanna and Amarilli, Antoine and Suchanek, Fabian M.}, LANGUAGE = {eng}, ISBN = {978-1-4503-3627-7}, DOI = {10.1145/2767109.2767116}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Proceedings of the 18th International Workshop on Web and Databases (WebDB 2015)}, EDITOR = {Stoyanovich, Julia and Suchanek, Fabian M.}, PAGES = {13--19}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Talaika, Aliaksandr %A Biega, Joanna %A Amarilli, Antoine %A Suchanek, Fabian M. %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Télécom ParisTech Télécom ParisTech %T IBEX: Harvesting Entities from the Web Using Unique Identifiers : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-AF0D-5 %R 10.1145/2767109.2767116 %D 2016 %B 18th International Workshop on the Web and Databases %Z date of event: 2015-05-31 - 2015-05-31 %C Melbourne, Australia %B Proceedings of the 18th International Workshop on Web and Databases %E Stoyanovich, Julia; Suchanek, Fabian M. %P 13 - 19 %I ACM %@ 978-1-4503-3627-7
[177]
N. Tandon, “Commonsense Knowledge Acquisition and Applications,” Universität des Saarlandes, Saarbrücken, 2016.
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@phdthesis{TandonPhD2016, TITLE = {Commonsense Knowledge Acquisition and Applications}, AUTHOR = {Tandon, Niket}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-66291}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Tandon, Niket %Y Weikum, Gerhard %A referee: Lieberman, Henry %A referee: Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Commonsense Knowledge Acquisition and Applications : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-78F6-A %U urn:nbn:de:bsz:291-scidok-66291 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P XIV, 154 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=dehttp://scidok.sulb.uni-saarland.de/volltexte/2016/6629/
[178]
N. Tandon, C. D. Hariman, J. Urbani, A. Rohrbach, M. Rohrbach, and G. Weikum, “Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags,” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 2016.
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@inproceedings{TandonAAAI2016, TITLE = {Commonsense in Parts: Mining Part-Whole Relations from the {Web} and Image Tags}, AUTHOR = {Tandon, Niket and Hariman, Charles Darwis and Urbani, Jacopo and Rohrbach, Anna and Rohrbach, Marcus and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-57735-760-5}, PUBLISHER = {AAAI Press}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence}, PAGES = {243--250}, ADDRESS = {Phoenix, AZ, USA}, }
Endnote
%0 Conference Proceedings %A Tandon, Niket %A Hariman, Charles Darwis %A Urbani, Jacopo %A Rohrbach, Anna %A Rohrbach, Marcus %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-ABFE-1 %D 2016 %B Thirtieth AAAI Conference on Artificial Intelligence %Z date of event: 2016-02-12 - 2016-02-17 %C Phoenix, AZ, USA %B Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence %P 243 - 250 %I AAAI Press %@ 978-1-57735-760-5 %U http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12337/11590
[179]
C. Teflioudi, “Algorithms for Shared-Memory Matrix Completion and Maximum Inner Product Search,” Universität des Saarlandes, Saarbrücken, 2016.
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@phdthesis{Teflioudiphd2016, TITLE = {Algorithms for Shared-Memory Matrix Completion and Maximum Inner Product Search}, AUTHOR = {Teflioudi, Christina}, LANGUAGE = {eng}, URL = {urn:nbn:de:bsz:291-scidok-64699}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Teflioudi, Christina %Y Gemulla, Rainer %A referee: Weikum, Gerhard %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Algorithms for Shared-Memory Matrix Completion and Maximum Inner Product Search : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-43FA-2 %U urn:nbn:de:bsz:291-scidok-64699 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P xi, 110 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=dehttp://scidok.sulb.uni-saarland.de/volltexte/2016/6469/
[180]
J. Urbani, S. Dutta, S. Gurajada, and G. Weikum, “KOGNAC: Efficient Encoding of Large Knowledge Graphs,” in Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2016), New York, NY, USA, 2016.
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@inproceedings{UrbaniIJCAI2016, TITLE = {{KOGNAC}: {E}fficient Encoding of Large Knowledge Graphs}, AUTHOR = {Urbani, Jacopo and Dutta, Sourav and Gurajada, Sairam and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-57735-771-1}, URL = {http://www.ijcai.org/Proceedings/16/Papers/548.pdf}, PUBLISHER = {AAAI}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2016)}, EDITOR = {Kambhampati, Subbarao}, PAGES = {3896--3902}, ADDRESS = {New York, NY, USA}, }
Endnote
%0 Conference Proceedings %A Urbani, Jacopo %A Dutta, Sourav %A Gurajada, Sairam %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T KOGNAC: Efficient Encoding of Large Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A641-2 %U http://www.ijcai.org/Proceedings/16/Papers/548.pdf %D 2016 %B 25th International Joint Conference on Artificial Intelligence %Z date of event: 2016-07-09 - 2016-07-15 %C New York, NY, USA %B Twenty-Fifth International Joint Conference on Artificial Intelligence %E Kambhampati, Subbarao %P 3896 - 3902 %I AAAI %@ 978-1-57735-771-1
[181]
J. Urbani, S. Dutta, S. Gurajada, and G. Weikum, “KOGNAC: Efficient Encoding of Large Knowledge Graphs,” 2016. [Online]. Available: http://arxiv.org/abs/1604.04795. (arXiv: 1604.04795)
Abstract
Many Web applications require efficient querying of large Knowledge Graphs (KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques. In KOGNAC, frequent terms are detected with a frequency approximation algorithm and encoded to maximise compression. Infrequent terms are semantically grouped into ontological classes and encoded to increase data locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines, and observed that it significantly improves SPARQL querying on KGs with up to 1B edges.
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@online{Urbani2016, TITLE = {{KOGNAC}: Efficient Encoding of Large Knowledge Graphs}, AUTHOR = {Urbani, Jacopo and Dutta, Sourav and Gurajada, Sairam and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1604.04795}, EPRINT = {1604.04795}, EPRINTTYPE = {arXiv}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Many Web applications require efficient querying of large Knowledge Graphs (KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques. In KOGNAC, frequent terms are detected with a frequency approximation algorithm and encoded to maximise compression. Infrequent terms are semantically grouped into ontological classes and encoded to increase data locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines, and observed that it significantly improves SPARQL querying on KGs with up to 1B edges.}, }
Endnote
%0 Report %A Urbani, Jacopo %A Dutta, Sourav %A Gurajada, Sairam %A Weikum, Gerhard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T KOGNAC: Efficient Encoding of Large Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-01C1-3 %U http://arxiv.org/abs/1604.04795 %D 2016 %X Many Web applications require efficient querying of large Knowledge Graphs (KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques. In KOGNAC, frequent terms are detected with a frequency approximation algorithm and encoded to maximise compression. Infrequent terms are semantically grouped into ontological classes and encoded to increase data locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines, and observed that it significantly improves SPARQL querying on KGs with up to 1B edges. %K Computer Science, Artificial Intelligence, cs.AI
[182]
G. Weikum, J. Hoffart, and F. Suchanek, “Ten Years of Knowledge Harvesting: Lessons and Challenges,” Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, vol. 39, no. 3, 2016.
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@article{Weikum_Hoffart_Suchanek2016, TITLE = {Ten Years of Knowledge Harvesting: {L}essons and Challenges}, AUTHOR = {Weikum, Gerhard and Hoffart, Johannes and Suchanek, Fabian}, LANGUAGE = {eng}, URL = {http://sites.computer.org/debull/A16sept/p41.pdf}, PUBLISHER = {IEEE Computer Society}, ADDRESS = {Los Alamitos, CA}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, JOURNAL = {Bulletin of the IEEE Computer Society Technical Committee on Data Engineering}, VOLUME = {39}, NUMBER = {3}, PAGES = {41--50}, }
Endnote
%0 Journal Article %A Weikum, Gerhard %A Hoffart, Johannes %A Suchanek, Fabian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Télécom ParisTech %T Ten Years of Knowledge Harvesting: Lessons and Challenges : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A618-F %U http://sites.computer.org/debull/A16sept/p41.pdf %7 2016 %D 2016 %J Bulletin of the IEEE Computer Society Technical Committee on Data Engineering %V 39 %N 3 %& 41 %P 41 - 50 %I IEEE Computer Society %C Los Alamitos, CA
[183]
G. Weikum, “Die Abteilung Datenbanken und Informationssysteme am Max-Planck-Institut für Informatik,” Datenbank Spektrum, vol. 16, no. 1, 2016.
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@article{WeikumDBSpektrum2016, TITLE = {{Die Abteilung Datenbanken und Informationssysteme am Max-Planck-Institut f{\"u}r Informatik}}, AUTHOR = {Weikum, Gerhard}, LANGUAGE = {deu}, DOI = {10.1007/s13222-016-0211-z}, PUBLISHER = {Springer}, ADDRESS = {Berlin}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, JOURNAL = {Datenbank Spektrum}, VOLUME = {16}, NUMBER = {1}, PAGES = {77--82}, }
Endnote
%0 Journal Article %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Die Abteilung Datenbanken und Informationssysteme am Max-Planck-Institut für Informatik : %G deu %U http://hdl.handle.net/11858/00-001M-0000-002B-0194-B %R 10.1007/s13222-016-0211-z %7 2016 %D 2016 %J Datenbank Spektrum %V 16 %N 1 %& 77 %P 77 - 82 %I Springer %C Berlin
[184]
B. A. Wójciak, “Spaghetti: Finding Storylines in Large Collections of Documents,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{WojciakMSc2016, TITLE = {Spaghetti: Finding Storylines in Large Collections of Documents}, AUTHOR = {W{\'o}jciak, Beata Anna}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Wójciak, Beata Anna %Y Vreeken, Jilles %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Spaghetti: Finding Storylines in Large Collections of Documents : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5F3F-3 %I Universität des Saarlandes %C Saarbrücken %D 2016 %V master %9 master
[185]
H. Wu, M. Sun, J. Vreeken, N. Tatti, C. North, and N. Ramakrishnan, “Interactive and Iterative Discovery of Entity Network Subgraphs,” 2016. [Online]. Available: http://arxiv.org/abs/1608.03889. (arXiv: 1608.03889)
Abstract
Graph mining to extract interesting components has been studied in various guises, e.g., communities, dense subgraphs, cliques. However, most existing works are based on notions of frequency and connectivity and do not capture subjective interestingness from a user's viewpoint. Furthermore, existing approaches to mine graphs are not interactive and cannot incorporate user feedbacks in any natural manner. In this paper, we address these gaps by proposing a graph maximum entropy model to discover surprising connected subgraph patterns from entity graphs. This model is embedded in an interactive visualization framework to enable human-in-the-loop, model-guided data exploration. Using case studies on real datasets, we demonstrate how interactions between users and the maximum entropy model lead to faster and explainable conclusions.
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@online{Wu1608.03889, TITLE = {Interactive and Iterative Discovery of Entity Network Subgraphs}, AUTHOR = {Wu, Hao and Sun, Maoyuan and Vreeken, Jilles and Tatti, Nikolaj and North, Chris and Ramakrishnan, Naren}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1608.03889}, EPRINT = {1608.03889}, EPRINTTYPE = {arXiv}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Graph mining to extract interesting components has been studied in various guises, e.g., communities, dense subgraphs, cliques. However, most existing works are based on notions of frequency and connectivity and do not capture subjective interestingness from a user's viewpoint. Furthermore, existing approaches to mine graphs are not interactive and cannot incorporate user feedbacks in any natural manner. In this paper, we address these gaps by proposing a graph maximum entropy model to discover surprising connected subgraph patterns from entity graphs. This model is embedded in an interactive visualization framework to enable human-in-the-loop, model-guided data exploration. Using case studies on real datasets, we demonstrate how interactions between users and the maximum entropy model lead to faster and explainable conclusions.}, }
Endnote
%0 Report %A Wu, Hao %A Sun, Maoyuan %A Vreeken, Jilles %A Tatti, Nikolaj %A North, Chris %A Ramakrishnan, Naren %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T Interactive and Iterative Discovery of Entity Network Subgraphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A939-F %U http://arxiv.org/abs/1608.03889 %D 2016 %X Graph mining to extract interesting components has been studied in various guises, e.g., communities, dense subgraphs, cliques. However, most existing works are based on notions of frequency and connectivity and do not capture subjective interestingness from a user's viewpoint. Furthermore, existing approaches to mine graphs are not interactive and cannot incorporate user feedbacks in any natural manner. In this paper, we address these gaps by proposing a graph maximum entropy model to discover surprising connected subgraph patterns from entity graphs. This model is embedded in an interactive visualization framework to enable human-in-the-loop, model-guided data exploration. Using case studies on real datasets, we demonstrate how interactions between users and the maximum entropy model lead to faster and explainable conclusions. %K cs.SI,Computer Science, Databases, cs.DB
[186]
H. Wu, Y. Ning, P. Chakraborty, J. Vreeken, N. Tatti, and N. Ramakrishnan, “Generating Realistic Synthetic Population Datasets,” 2016. [Online]. Available: http://arxiv.org/abs/1602.06844. (arXiv: 1602.06844)
Abstract
Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and on US census datasets, and demonstrate its feasibility using an epidemic simulation application.
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@online{Wu_arXiv2016, TITLE = {Generating Realistic Synthetic Population Datasets}, AUTHOR = {Wu, Hao and Ning, Yue and Chakraborty, Prithwish and Vreeken, Jilles and Tatti, Nikolaj and Ramakrishnan, Naren}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1602.06844}, EPRINT = {1602.06844}, EPRINTTYPE = {arXiv}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and on US census datasets, and demonstrate its feasibility using an epidemic simulation application.}, }
Endnote
%0 Report %A Wu, Hao %A Ning, Yue %A Chakraborty, Prithwish %A Vreeken, Jilles %A Tatti, Nikolaj %A Ramakrishnan, Naren %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Generating Realistic Synthetic Population Datasets : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-08F9-B %U http://arxiv.org/abs/1602.06844 %D 2016 %X Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and on US census datasets, and demonstrate its feasibility using an epidemic simulation application. %K Computer Science, Databases, cs.DB
[187]
M. Yahya, D. Barbosa, K. Berberich, Q. Wang, and G. Weikum, “Relationship Queries on Extended Knowledge Graphs,” in WSDM’16, 9th ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA, 2016.
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@inproceedings{YahyaWSDM2016, TITLE = {Relationship Queries on Extended Knowledge Graphs}, AUTHOR = {Yahya, Mohamed and Barbosa, Denilson and Berberich, Klaus and Wang, Quiyue and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-3716-8}, DOI = {10.1145/2835776.2835795}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WSDM'16, 9th ACM International Conference on Web Search and Data Mining}, PAGES = {605--614}, ADDRESS = {San Francisco, CA, USA}, }
Endnote
%0 Conference Proceedings %A Yahya, Mohamed %A Barbosa, Denilson %A Berberich, Klaus %A Wang, Quiyue %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Relationship Queries on Extended Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-ABAA-0 %R 10.1145/2835776.2835795 %D 2016 %B 9th ACM International Conference on Web Search and Data Mining %Z date of event: 2016-02-22 - 2016-02-25 %C San Francisco, CA, USA %B WSDM'16 %P 605 - 614 %I ACM %@ 978-1-4503-3716-8
[188]
M. Yahya, “Question Answering and Query Processing for Extended Knowledge Graphs,” Universität des Saarlandes, Saarbrücken, 2016.
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@phdthesis{yahyaphd2016, TITLE = {Question Answering and Query Processing for Extended Knowledge Graphs}, AUTHOR = {Yahya, Mohamed}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Yahya, Mohamed %Y Weikum, Gerhard %A referee: Schütze, Hinrich %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Question Answering and Query Processing for Extended Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-48C2-7 %I Universität des Saarlandes %C Saarbrücken %D 2016 %P x, 160 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=dehttp://scidok.sulb.uni-saarland.de/volltexte/2016/6476/
[189]
M. Yahya, K. Berberich, M. Ramanath, and G. Weikum, “Exploratory Querying of Extended Knowledge Graphs,” Proceedings of the VLDB Endowment (Proc. VLDB 2016), vol. 9, no. 1, 2016.
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@article{YahyaVLDB2016, TITLE = {Exploratory Querying of Extended Knowledge Graphs}, AUTHOR = {Yahya, Mohamed and Berberich, Klaus and Ramanath, Maya and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, JOURNAL = {Proceedings of the VLDB Endowment (Proc. VLDB)}, VOLUME = {9}, NUMBER = {1}, PAGES = {1521--1524}, BOOKTITLE = {Proceedings of the 42nd International Conference on Very Large Data Bases (VLDB 2016)}, EDITOR = {Chaudhuri, Surajit and Haritsa, Jayant}, }
Endnote
%0 Journal Article %A Yahya, Mohamed %A Berberich, Klaus %A Ramanath, Maya %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exploratory Querying of Extended Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A61C-7 %7 2016 %D 2016 %J Proceedings of the VLDB Endowment %O PVLDB %V 9 %N 1 %& 1521 %P 1521 - 1524 %I ACM %C New York, NY %B Proceedings of the 42nd International Conference on Very Large Data Bases %O VLDB 2016 New Delhi, India, September 5 - 9, 2016 %U http://www.vldb.org/pvldb/vol9/p1521-yahya.pdf
[190]
L. Zervakis, C. Tryfonopoulos, V. Setty, S. Seufert, and S. Skiadopoulos, “Towards Publish/Subscribe Functionality on Graphs,” in Proceedings of the Workshops of the EDBT/ICDT 2016 Joint Conference, Bordeaux, France, 2016.
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@inproceedings{DBLP:conf/edbt/ZervakisTSSS16, TITLE = {Towards Publish/Subscribe Functionality on Graphs}, AUTHOR = {Zervakis, Lefteris and Tryfonopoulos, Christos and Setty, Vinay and Seufert, Stephan and Skiadopoulos, Spiros}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {urn:nbn:de:0074-1558-2}, PUBLISHER = {CEUR-WS.org}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Workshops of the EDBT/ICDT 2016 Joint Conference}, EDITOR = {Palpanas, Thermis and Stefanidis, Kostas}, EID = {13}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {1558}, ADDRESS = {Bordeaux, France}, }
Endnote
%0 Conference Proceedings %A Zervakis, Lefteris %A Tryfonopoulos, Christos %A Setty, Vinay %A Seufert, Stephan %A Skiadopoulos, Spiros %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Towards Publish/Subscribe Functionality on Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1CAE-1 %D 2016 %B 2nd International Workshop on Preservation of Evolving Big Data %Z date of event: 2016-03-15 - 2016-03-15 %C Bordeaux, France %B Proceedings of the Workshops of the EDBT/ICDT 2016 Joint Conference %E Palpanas, Thermis; Stefanidis, Kostas %Z sequence number: 13 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 1558 %@ false
[191]
H. Zhang and V. Setty, “Finding Diverse Needles in a Haystack of Comments -- Social Media Exploration for News,” in WebSci’16, ACM Web Science Conference, Hannover, Germany, 2016.
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@inproceedings{ZhangWebSci2016, TITLE = {Finding Diverse Needles in a Haystack of Comments -- Social Media Exploration for News}, AUTHOR = {Zhang, Hang and Setty, Vinay}, LANGUAGE = {eng}, ISBN = {978-1-4503-4208-7}, DOI = {10.1145/2908131.2908168}, PUBLISHER = {ACM}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, BOOKTITLE = {WebSci'16, ACM Web Science Conference}, PAGES = {286--290}, ADDRESS = {Hannover, Germany}, }
Endnote
%0 Conference Proceedings %A Zhang, Hang %A Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Finding Diverse Needles in a Haystack of Comments -- Social Media Exploration for News : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-020A-C %R 10.1145/2908131.2908168 %D 2016 %B ACM Web Science Conference %Z date of event: 2016-05-22 - 2016-05-25 %C Hannover, Germany %B WebSci'16 %P 286 - 290 %I ACM %@ 978-1-4503-4208-7
[192]
H. Zhang, “Diversified Social Media Retrieval for News Stories,” Universität des Saarlandes, Saarbrücken, 2016.
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@mastersthesis{ZhangMSc2016, TITLE = {Diversified Social Media Retrieval for News Stories}, AUTHOR = {Zhang, Hang}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2016}, MARGINALMARK = {$\bullet$}, DATE = {2016}, }
Endnote
%0 Thesis %A Zhang, Hang %Y Neumann, Günther %A referee: Weikum, Gerhard %A referee: Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Diversified Social Media Retrieval for News Stories : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-48D3-E %I Universität des Saarlandes %C Saarbrücken %D 2016 %V master %9 master
2015
[193]
S. Abiteboul, L. Dong, O. Etzioni, D. Srivastava, G. Weikum, J. Stoyanovich, and F. M. Suchanek, “The Elephant in the Room: Getting Value from Big Data,” in Proceedings of the 18th International Workshop on Web and Databases (WebDB 2015), Melbourne, Australia, 2015.
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@inproceedings{AbiteboulWebDB2015, TITLE = {The Elephant in the Room: {G}etting Value from {Big Data}}, AUTHOR = {Abiteboul, Serge and Dong, Luna and Etzioni, Oren and Srivastava, Divesh and Weikum, Gerhard and Stoyanovich, Julia and Suchanek, Fabian M.}, LANGUAGE = {eng}, ISBN = {978-1-4503-3627-7}, DOI = {10.1145/2767109.2770014}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Proceedings of the 18th International Workshop on Web and Databases (WebDB 2015)}, EDITOR = {Stoyanovich, Julia and Suchanek, Fabian M.}, PAGES = {1--5}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Abiteboul , Serge %A Dong, Luna %A Etzioni, Oren %A Srivastava, Divesh %A Weikum, Gerhard %A Stoyanovich, Julia %A Suchanek, Fabian M. %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Télécom ParisTech %T The Elephant in the Room: Getting Value from Big Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0027-D3F2-F %R 10.1145/2767109.2770014 %D 2015 %B 18th International Workshop on the Web and Databases %Z date of event: 2015-05-31 - 2015-05-31 %C Melbourne, Australia %B Proceedings of the 18th International Workshop on Web and Databases %E Stoyanovich, Julia; Suchanek, Fabian M. %P 1 - 5 %I ACM %@ 978-1-4503-3627-7
[194]
A. Abujabal, “Mining Past, Present, and Future,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{AbujabalMaster2015, TITLE = {Mining Past, Present, and Future}, AUTHOR = {Abujabal, Abdalghani}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Abujabal, Abdalghani %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mining Past, Present, and Future : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0025-A974-2 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P XII, 86 p. %V master %9 master
[195]
A. Anagnostopoulos, L. Becchetti, I. Bordino, S. Leonardi, I. Mele, and P. Sankowski, “Stochastic Query Covering for Fast Approximate Document Retrieval,” ACM Transactions on Information Systems, vol. 33, no. 3, 2015.
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@article{Anagnostopoulos:TOIS, TITLE = {Stochastic Query Covering for Fast Approximate Document Retrieval}, AUTHOR = {Anagnostopoulos, Aris and Becchetti, Luca and Bordino, Ilaria and Leonardi, Stefano and Mele, Ida and Sankowski, Piotr}, LANGUAGE = {eng}, ISSN = {1046-8188}, DOI = {10.1145/2699671}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, JOURNAL = {ACM Transactions on Information Systems}, VOLUME = {33}, NUMBER = {3}, PAGES = {1--35}, EID = {11}, }
Endnote
%0 Journal Article %A Anagnostopoulos, Aris %A Becchetti, Luca %A Bordino, Ilaria %A Leonardi, Stefano %A Mele, Ida %A Sankowski, Piotr %+ External Organizations External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Stochastic Query Covering for Fast Approximate Document Retrieval : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-B6C7-2 %R 10.1145/2699671 %7 2015 %D 2015 %J ACM Transactions on Information Systems %O TOIS %V 33 %N 3 %& 1 %P 1 - 35 %Z sequence number: 11 %I ACM %C New York, NY %@ false
[196]
A. Anagnostopoulos, L. Becchetti, A. Fazzone, I. Mele, and M. Riondato, “The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing,” in SIGMOD’15, ACM SIGMOD International Conference on Management of Data, Melbourne, Australia, 2015.
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@inproceedings{Anagnostopoulos:SIGMOD2015, TITLE = {The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing}, AUTHOR = {Anagnostopoulos, Aris and Becchetti, Luca and Fazzone, Adriano and Mele, Ida and Riondato, Matteo}, LANGUAGE = {eng}, ISBN = {978-1-4503-2758-9}, DOI = {10.1145/2723372.2723722}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {SIGMOD'15, ACM SIGMOD International Conference on Management of Data}, PAGES = {983--998}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Anagnostopoulos, Aris %A Becchetti, Luca %A Fazzone, Adriano %A Mele, Ida %A Riondato, Matteo %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T The Importance of Being Expert: Efficient Max-Finding in Crowdsourcing : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-B6BE-7 %R 10.1145/2723372.2723722 %D 2015 %B ACM SIGMOD International Conference on Management of Data %Z date of event: 2015-05-31 - 2015-06-04 %C Melbourne, Australia %B SIGMOD'15 %P 983 - 998 %I ACM %@ 978-1-4503-2758-9
[197]
H. R. Bazoobandi, S. de Rooij, J. Urbani, A. ten Teije, F. van Harmelen, and H. Bal, “A Compact In-Memory Dictionary for RDF Data,” in The Semantic Web (ESWC 2015), Portorož, Slovenia, 2015.
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@inproceedings{Urbanilncs15, TITLE = {A Compact In-Memory Dictionary for {RDF} Data}, AUTHOR = {Bazoobandi, Hamid R. and de Rooij, Steve and Urbani, Jacopo and ten Teije, Annette and van Harmelen, Frank and Bal, Henri}, LANGUAGE = {eng}, ISBN = {978-3-319-18817-1}, DOI = {10.1007/978-3-319-18818-8_13}, PUBLISHER = {Springer}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {The Semantic Web (ESWC 2015)}, EDITOR = {Gandon, Fabien and Sabou, Marta and Sack, Harald and d'Amato, Claudia and Cudr{\'e}-Mauroux, Philippe and Zimmermann, Antoine}, PAGES = {205--220}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9088}, ADDRESS = {Portoro{\v z}, Slovenia}, }
Endnote
%0 Conference Proceedings %A Bazoobandi, Hamid R. %A de Rooij, Steve %A Urbani, Jacopo %A ten Teije, Annette %A van Harmelen, Frank %A Bal, Henri %+ External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T A Compact In-Memory Dictionary for RDF Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-F1A6-9 %R 10.1007/978-3-319-18818-8_13 %D 2015 %B 12th European Semantic Web Conference %Z date of event: 2015-05-31 - 2015-06-04 %C Portorož, Slovenia %B The Semantic Web %E Gandon, Fabien; Sabou, Marta; Sack, Harald; d'Amato, Claudia; Cudré-Mauroux, Philippe; Zimmermann, Antoine %P 205 - 220 %I Springer %@ 978-3-319-18817-1 %B Lecture Notes in Computer Science %N 9088
[198]
K. Beedkar, K. Berberich, R. Gemulla, and I. Miliaraki, “Closing the Gap: Sequence Mining at Scale,” ACM Transactions on Database Systems, vol. 40, no. 2, 2015.
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@article{DBLP:journals/tods/BeedkarBGM15, TITLE = {Closing the Gap: {S}equence Mining at Scale}, AUTHOR = {Beedkar, Kaustubh and Berberich, Klaus and Gemulla, Rainer and Miliaraki, Iris}, LANGUAGE = {eng}, ISSN = {0362-5915}, DOI = {10.1145/2757217}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, JOURNAL = {ACM Transactions on Database Systems}, VOLUME = {40}, NUMBER = {2}, PAGES = {1--44}, EID = {8}, }
Endnote
%0 Journal Article %A Beedkar, Kaustubh %A Berberich, Klaus %A Gemulla, Rainer %A Miliaraki, Iris %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Closing the Gap: Sequence Mining at Scale : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-5712-1 %R 10.1145/2757217 %7 2015 %D 2015 %J ACM Transactions on Database Systems %V 40 %N 2 %& 1 %P 1 - 44 %Z sequence number: 8 %I ACM %C New York, NY %@ false
[199]
A. Biswas, “Retrieving Web User’s Personal Information,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{BiswasMSc2015, TITLE = {Retrieving Web User's Personal Information}, AUTHOR = {Biswas, Angeeka}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Biswas, Angeeka %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Retrieving Web User's Personal Information : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-48D1-1 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P VIII, 30 p. %V master %9 master
[200]
K. Budhathoki and J. Vreeken, “The Difference and the Norm - Characterising Similarities and Differences Between Databases,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, 2015.
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@inproceedings{BudhathokiECML2015, TITLE = {The Difference and the Norm -- Characterising Similarities and Differences Between Databases}, AUTHOR = {Budhathoki, Kailash and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-3-319-23524-0}, DOI = {10.1007/978-3-319-23525-7_13}, PUBLISHER = {Springer}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2015)}, EDITOR = {Appice, Annalisa and Pereira Rodrigues, Pedro and Gama, Jo{\~a}o and Al{\'i}pio, Jorge and Soares, Carlos}, PAGES = {206--223}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {9285}, ADDRESS = {Porto, Portugal}, }
Endnote
%0 Conference Proceedings %A Budhathoki, Kailash %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T The Difference and the Norm - Characterising Similarities and Differences Between Databases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-2271-F %R 10.1007/978-3-319-23525-7_13 %D 2015 %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases %Z date of event: 2015-09-07 - 2015-09-11 %C Porto, Portugal %B Machine Learning and Knowledge Discovery in Databases %E Appice, Annalisa; Pereira Rodrigues, Pedro; Gama, João; Alípio, Jorge; Soares, Carlos %P 206 - 223 %I Springer %@ 978-3-319-23524-0 %B Lecture Notes in Artificial Intelligence %N 9285
[201]
K. Budhathoki, “Correlation by Compression,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{BudhathokiMaster2015, TITLE = {Correlation by Compression}, AUTHOR = {Budhathoki, Kailash}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Budhathoki, Kailash %Y Vreeken, Jilles %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Correlation by Compression : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-0753-D %I Universität des Saarlandes %C Saarbrücken %D 2015 %P X, 56 p. %V master %9 master
[202]
K. Chakrabarti, “K-Shortest Paths with Overlap Constraints,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{ChakrabartiMSc2015, TITLE = {K-Shortest Paths with Overlap Constraints}, AUTHOR = {Chakrabarti, Kaustuv}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Chakrabarti, Kaustuv %Y Weikum, Gerhard %A referee: Setty, Vinay %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T K-Shortest Paths with Overlap Constraints : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-43A5-D %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 54 p. %V master %9 master
[203]
D. Dedik, “Robust Type Classification of Out of Knowledge Base Entities,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{DedikMaster2015, TITLE = {Robust Type Classification of Out of Knowledge Base Entities}, AUTHOR = {Dedik, Darya}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Dedik, Darya %Y Weikum, Gerhard %A referee: Spaniol, Marc %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Robust Type Classification of Out of Knowledge Base Entities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0026-C0EC-F %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 65 p. %V master %9 master
[204]
L. Del Corro, A. Abujabal, R. Gemulla, and G. Weikum, “FINET: Context-Aware Fine-Grained Named Entity Typing,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), Lisbon, Portugal, 2015.
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@inproceedings{delcorro-EtAl:2015:EMNLP, TITLE = {{FINET}: {C}ontext-Aware Fine-Grained Named Entity Typing}, AUTHOR = {Del Corro, Luciano and Abujabal, Abdalghani and Gemulla, Rainer and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-941643-32-7}, URL = {https://aclweb.org/anthology/D/D15/D15-1103}, PUBLISHER = {ACL}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)}, PAGES = {868--878}, ADDRESS = {Lisbon, Portugal}, }
Endnote
%0 Conference Proceedings %A Del Corro, Luciano %A Abujabal, Abdalghani %A Gemulla, Rainer %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T FINET: Context-Aware Fine-Grained Named Entity Typing : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-49C3-C %U https://aclweb.org/anthology/D/D15/D15-1103 %D 2015 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2015-09-17 - 2015-09-21 %C Lisbon, Portugal %B Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing %P 868 - 878 %I ACL %@ 978-1-941643-32-7 %U https://www.cs.cmu.edu/~ark/EMNLP-2015/proceedings/EMNLP/pdf/EMNLP103.pdf
[205]
S. Dutta, S. Bhattacherjee, and A. Narang, “Mining Wireless Intelligence using Unsupervised Edge and Core Analytics,” in 2nd Workshop on Smarter Planet and Big Data Analytics, Goa, Indien. (Accepted/in press)
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@inproceedings{SouSPBDA2015, TITLE = {Mining Wireless Intelligence using Unsupervised Edge and Core Analytics}, AUTHOR = {Dutta, Sourav and Bhattacherjee, Souvik and Narang, Ankur}, LANGUAGE = {eng}, YEAR = {2015}, PUBLREMARK = {Accepted}, BOOKTITLE = {2nd Workshop on Smarter Planet and Big Data Analytics}, ADDRESS = {Goa, Indien}, }
Endnote
%0 Conference Proceedings %A Dutta, Sourav %A Bhattacherjee, Souvik %A Narang, Ankur %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Mining Wireless Intelligence using Unsupervised Edge and Core Analytics : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-54B5-0 %D 2014 %B 2nd Workshop on Smarter Planet and Big Data Analytics %Z date of event: 2015-01-04 - 2015-01-07 %C Goa, Indien %B 2nd Workshop on Smarter Planet and Big Data Analytics
[206]
S. Dutta, “MIST: Top-k Approximate Sub-String Mining using Triplet Statistical Significance,” in Advances in Information Retrieval (ECIR 2015), Vienna, Austria, 2015.
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@inproceedings{SouECIR2015, TITLE = {{MIST}: Top-k Approximate Sub-String Mining using Triplet Statistical Significance}, AUTHOR = {Dutta, Sourav}, LANGUAGE = {eng}, ISBN = {978-3-319-16353-6}, DOI = {10.1007/978-3-319-16354-3_31}, PUBLISHER = {Springer}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2015)}, EDITOR = {Hanbury, Allan and Kazai, Gabriella and Rauber, Andreas and Fuhr, Norbert}, PAGES = {284--290}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9022}, ADDRESS = {Vienna, Austria}, }
Endnote
%0 Conference Proceedings %A Dutta, Sourav %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T MIST: Top-k Approximate Sub-String Mining using Triplet Statistical Significance : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-54B2-5 %R 10.1007/978-3-319-16354-3_31 %D 2015 %B 37th European Conference on Information Retrieval %Z date of event: 2015-03-29 - 2015-04-02 %C Vienna, Austria %B Advances in Information Retrieval %E Hanbury, Allan; Kazai, Gabriella; Rauber, Andreas; Fuhr, Norbert %P 284 - 290 %I Springer %@ 978-3-319-16353-6 %B Lecture Notes in Computer Science %N 9022
[207]
S. Dutta, A. Narang, and S. Bhattacherjee, “Predictive Caching Framework for Mobile Wireless Networks,” in MDM 2015, 16th International Conference on Mobile Data Management, Pittsburgh, PA, USA, 2015.
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@inproceedings{DuttaMDM2015, TITLE = {Predictive Caching Framework for Mobile Wireless Networks}, AUTHOR = {Dutta, Sourav and Narang, Ankur and Bhattacherjee, Souvik}, LANGUAGE = {eng}, ISBN = {978-1-4799-9972-9}, DOI = {10.1109/MDM.2015.14}, PUBLISHER = {IEEE}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {MDM 2015, 16th International Conference on Mobile Data Management}, PAGES = {179--184}, ADDRESS = {Pittsburgh, PA, USA}, }
Endnote
%0 Conference Proceedings %A Dutta, Sourav %A Narang, Ankur %A Bhattacherjee, Souvik %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Predictive Caching Framework for Mobile Wireless Networks : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002B-A5AF-3 %R 10.1109/MDM.2015.14 %D 2015 %B 16th International Conference on Mobile Data Management %Z date of event: 2015-06-15 - 2015-06-18 %C Pittsburgh, PA, USA %B MDM 2015 %P 179 - 184 %I IEEE %@ 978-1-4799-9972-9
[208]
S. Dutta and G. Weikum, “C3EL: A Joint Model for Cross-Document Co-Reference Resolution and Entity Linking,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), Lisbon, Portugal, 2015.
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@inproceedings{dutta-weikum:2015:EMNLP, TITLE = {{C3EL}: {A} Joint Model for Cross-Document Co-Reference Resolution and Entity Linking}, AUTHOR = {Dutta, Sourav and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-941643-32-7}, URL = {https://aclweb.org/anthology/D/D15/D15-1101}, PUBLISHER = {ACL}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)}, PAGES = {846--856}, ADDRESS = {Lisbon, Portugal}, }
Endnote
%0 Conference Proceedings %A Dutta, Sourav %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T C3EL: A Joint Model for Cross-Document Co-Reference Resolution and Entity Linking : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-49C1-0 %U https://aclweb.org/anthology/D/D15/D15-1101 %D 2015 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2015-09-17 - 2015-09-21 %C Lisbon, Portugal %B Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing %P 846 - 856 %I ACL %@ 978-1-941643-32-7 %U https://www.cs.cmu.edu/~ark/EMNLP-2015/proceedings/EMNLP/pdf/EMNLP101.pdf
[209]
P. Ernst, A. Siu, and G. Weikum, “KnowLife: A Versatile Approach for Constructing a Large Knowledge Graph for Biomedical Sciences,” BMC Bioinformatics, vol. 16, no. 1, 2015.
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@article{ErnstSiuWeikum2015, TITLE = {{KnowLife}: A Versatile Approach for Constructing a Large Knowledge Graph for Biomedical Sciences}, AUTHOR = {Ernst, Patrick and Siu, Amy and Weikum, Gerhard}, LANGUAGE = {eng}, ISSN = {1471-2105}, URL = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4448285&tool=pmcentrez&rendertype=abstract}, DOI = {10.1186/s12859-015-0549-5}, PUBLISHER = {BioMed Central}, ADDRESS = {London}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, JOURNAL = {BMC Bioinformatics}, VOLUME = {16}, NUMBER = {1}, EID = {157}, }
Endnote
%0 Journal Article %A Ernst, Patrick %A Siu, Amy %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T KnowLife: A Versatile Approach for Constructing a Large Knowledge Graph for Biomedical Sciences : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0027-7AB7-0 %F OTHER: pmcidPMC4448285 %F OTHER: pmc-uid4448285 %F OTHER: publisher-id549 %R 10.1186/s12859-015-0549-5 %U http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4448285&tool=pmcentrez&rendertype=abstract %7 2015-05-14 %D 2015 %8 14.05.2015 %K Relation extraction %J BMC Bioinformatics %V 16 %N 1 %Z sequence number: 157 %I BioMed Central %C London %@ false
[210]
M. Gad-Elrab, “AIDArabic+ Named Entity Disambiguation for Arabic Text,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{Gad-ElrabMaster2015, TITLE = {{AIDArabic}+ Named Entity Disambiguation for Arabic Text}, AUTHOR = {Gad-Elrab, Mohamed}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Gad-Elrab, Mohamed %Y Weikum, Gerhard %A referee: Berberich, Klaus %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T AIDArabic+ Named Entity Disambiguation for Arabic Text : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-0F70-5 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 56 p. %V master %9 master
[211]
M. H. Gad-Elrab, M. A. Yosef, and G. Weikum, “Named Entity Disambiguation for Resource-poor Languages,” in ESAIR’15, Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval, Melbourne, Australia, 2015.
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@inproceedings{Gad-ElrabESAIR2015, TITLE = {Named Entity Disambiguation for Resource-poor Languages}, AUTHOR = {Gad-Elrab, Mohamed H. and Yosef, Mohamed Amir and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-3790-8}, DOI = {10.1145/2810133.2810138}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {ESAIR'15, Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval}, EDITOR = {Alonso, Omar and Kamps, Jaap and Karlgren, Jussi}, PAGES = {29--34}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Gad-Elrab, Mohamed H. %A Yosef, Mohamed Amir %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Named Entity Disambiguation for Resource-poor Languages : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-077F-B %R 10.1145/2810133.2810138 %D 2015 %B Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval %Z date of event: 2015-10-23 - 2015-10-23 %C Melbourne, Australia %B ESAIR'15 %E Alonso, Omar; Kamps, Jaap; Karlgren, Jussi %P 29 - 34 %I ACM %@ 978-1-4503-3790-8
[212]
M. H. Gad-Elrab, M. A. Yosef, and G. Weikum, “EDRAK: Entity-Centric Data Resource for Arabic Knowledge,” in The Second Workshop on Arabic Natural Language Processing (ANLP 2015), Beijing, China, 2015.
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@inproceedings{Gad-ElrabAnLP2015, TITLE = {{EDRAK}: {E}ntity-Centric Data Resource for {Arabic} Knowledge}, AUTHOR = {Gad-Elrab, Mohamed H. and Yosef, Mohamed Amir and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-941643-58-7}, PUBLISHER = {ACL}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The Second Workshop on Arabic Natural Language Processing (ANLP 2015)}, PAGES = {191--200}, ADDRESS = {Beijing, China}, }
Endnote
%0 Conference Proceedings %A Gad-Elrab, Mohamed H. %A Yosef, Mohamed Amir %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T EDRAK: Entity-Centric Data Resource for Arabic Knowledge : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002A-0773-3 %D 2015 %B The Second Workshop on Arabic Natural Language Processing %Z date of event: 2015-07-26 - 2015-07-31 %C Beijing, China %B The Second Workshop on Arabic Natural Language Processing %P 191 - 200 %I ACL %@ 978-1-941643-58-7
[213]
A. Grycner, G. Weikum, J. Pujara, J. Foulds, and L. Getoor, “RELLY: Inferring Hypernym Relationships Between Relational Phrases,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), Lisbon, Portugal, 2015.
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@inproceedings{grycner-EtAl:2015:EMNLP, TITLE = {{RELLY}: {I}nferring Hypernym Relationships Between Relational Phrases}, AUTHOR = {Grycner, Adam and Weikum, Gerhard and Pujara, Jay and Foulds, James and Getoor, Lise}, LANGUAGE = {eng}, ISBN = {978-1-941643-32-7}, URL = {http://aclweb.org/anthology/D15-1113}, PUBLISHER = {ACL}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)}, PAGES = {971--981}, ADDRESS = {Lisbon, Portugal}, }
Endnote
%0 Conference Proceedings %A Grycner, Adam %A Weikum, Gerhard %A Pujara, Jay %A Foulds, James %A Getoor, Lise %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations %T RELLY: Inferring Hypernym Relationships Between Relational Phrases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-49B0-5 %U http://aclweb.org/anthology/D15-1113 %D 2015 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2015-09-17 - 2015-09-21 %C Lisbon, Portugal %B Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing %P 971 - 981 %I ACL %@ 978-1-941643-32-7 %U https://www.cs.cmu.edu/~ark/EMNLP-2015/proceedings/EMNLP/pdf/EMNLP113.pdf
[214]
D. Gupta and K. Berberich, “Temporal Query Classification at Different Granularities,” in String Processing and Information Retrieval (SPIRE 2015), London, UK, 2015.
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@inproceedings{spire15-gupta, TITLE = {Temporal Query Classification at Different Granularities}, AUTHOR = {Gupta, Dhruv and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-23825-8}, DOI = {10.1007/978-3-319-23826-5_16}, PUBLISHER = {Springer}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {String Processing and Information Retrieval (SPIRE 2015)}, EDITOR = {Iliopoulos, Costas S. and Publisi, Simon J. and Yilmaz, Emine}, PAGES = {137--148}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9309}, ADDRESS = {London, UK}, }
Endnote
%0 Conference Proceedings %A Gupta, Dhruv %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Temporal Query Classification at Different Granularities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-4249-D %R 10.1007/978-3-319-23826-5_16 %D 2015 %B 22nd International Symposium on String Processing and Information Retrieval %Z date of event: 2015-08-31 - 2015-09-02 %C London, UK %B String Processing and Information Retrieval %E Iliopoulos, Costas S.; Publisi, Simon J.; Yilmaz, Emine %P 137 - 148 %I Springer %@ 978-3-319-23825-8 %B Lecture Notes in Computer Science %N 9309
[215]
C. D. Hariman, “Part-Whole Commonsense Knowledge Harvesting from the Web,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{HarimanMaster2015, TITLE = {Part-Whole Commonsense Knowledge Harvesting from the Web}, AUTHOR = {Hariman, Charles Darwis}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Hariman, Charles Darwis %Y Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Part-Whole Commonsense Knowledge Harvesting from the Web : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0026-C0E6-C %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 53 p. %V master %9 master
[216]
J. Hoffart, “Discovering and Disambiguating Named Entities in Text,” Universität des Saarlandes, Saarbrücken, 2015.
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@phdthesis{Hoffartthesis, TITLE = {Discovering and Disambiguating Named Entities in Text}, AUTHOR = {Hoffart, Johannes}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Hoffart, Johannes %Y Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering and Disambiguating Named Entities in Text : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0025-6C44-0 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P X, 103 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=dehttp://scidok.sulb.uni-saarland.de/volltexte/2015/6022/
[217]
J. Hoffart, N. Preda, F. M. Suchanek, and G. Weikum, “Knowledge Bases for Web Content Analytics,” in WWW’15 Companion, Florence, Italy, 2015.
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@inproceedings{hoffart2015knowledgebases, TITLE = {Knowledge Bases for {Web} Content Analytics}, AUTHOR = {Hoffart, Johannes and Preda, Nicoleta and Suchanek, Fabian M. and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-3473-0}, DOI = {10.1145/2740908.2741984}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {WWW'15 Companion}, PAGES = {1535--1535}, ADDRESS = {Florence, Italy}, }
Endnote
%0 Conference Proceedings %A Hoffart, Johannes %A Preda, Nicoleta %A Suchanek, Fabian M. %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowledge Bases for Web Content Analytics : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-8E68-7 %R 10.1145/2740908.2741984 %D 2015 %B 24th International Conference on World Wide Web %Z date of event: 2015-05-18 - 2015-05-22 %C Florence, Italy %B WWW'15 Companion %P 1535 - 1535 %I ACM %@ 978-1-4503-3473-0
[218]
K. Hui and K. Berberich, “Selective Labeling and Incomplete Label Mitigation for Low-Cost Evaluation,” in String Processing and Information Retrieval (SPIRE 2015), London, UK, 2015.
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@inproceedings{spire15-kaihui, TITLE = {Selective Labeling and Incomplete Label Mitigation for Low-Cost Evaluation}, AUTHOR = {Hui, Kai and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-3-319-23825-8}, DOI = {10.1007/978-3-319-23826-5_14}, PUBLISHER = {Springer}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {String Processing and Information Retrieval (SPIRE 2015)}, EDITOR = {Iliopoulos, Costas S. and Publisi, Simon J. and Yilmaz, Emine}, PAGES = {137--148}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9309}, ADDRESS = {London, UK}, }
Endnote
%0 Conference Proceedings %A Hui, Kai %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Selective Labeling and Incomplete Label Mitigation for Low-Cost Evaluation : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-5DAA-5 %R 10.1007/978-3-319-23826-5_14 %D 2015 %B 22nd International Symposium on String Processing and Information Retrieval %Z date of event: 2015-08-31 - 2015-09-02 %C London, UK %B String Processing and Information Retrieval %E Iliopoulos, Costas S.; Publisi, Simon J.; Yilmaz, Emine %P 137 - 148 %I Springer %@ 978-3-319-23825-8 %B Lecture Notes in Computer Science %N 9309
[219]
S. Karaev, P. Miettinen, and J. Vreeken, “Getting to Know the Unknown Unknowns: Destructive-noise Resistant Boolean Matrix Factorization,” in Proceedings of the 2015 SIAM International Conference on Data Mining (SDM 2015), Vancouver, Canada, 2015.
Abstract
Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.
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@inproceedings{karaev15getting, TITLE = {Getting to Know the Unknown Unknowns: {D}estructive-noise Resistant {Boolean} Matrix Factorization}, AUTHOR = {Karaev, Sanjar and Miettinen, Pauli and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-401-0}, DOI = {10.1137/1.9781611974010.37}, PUBLISHER = {SIAM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, ABSTRACT = {Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.}, BOOKTITLE = {Proceedings of the 2015 SIAM International Conference on Data Mining (SDM 2015)}, EDITOR = {Venkatasubramanian, Suresh and Ye, Jieping}, PAGES = {325--333}, ADDRESS = {Vancouver, Canada}, }
Endnote
%0 Conference Proceedings %A Karaev, Sanjar %A Miettinen, Pauli %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Getting to Know the Unknown Unknowns: Destructive-noise Resistant Boolean Matrix Factorization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-6C59-C %R 10.1137/1.9781611974010.37 %D 2015 %B 15th SIAM International Conference on Data Mining %Z date of event: 2015-04-30 - 2015-05-02 %C Vancouver, Canada %X Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data. %B Proceedings of the 2015 SIAM International Conference on Data Mining %E Venkatasubramanian, Suresh; Ye, Jieping %P 325 - 333 %I SIAM %@ 978-1-61197-401-0
[220]
A. Kopali, “Mitigation of Privacy Risk for Search Queries,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{KopaliMSc2015, TITLE = {Mitigation of Privacy Risk for Search Queries}, AUTHOR = {Kopali, Agim}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Kopali, Agim %Y Weikum, Gerhard %A referee: Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mitigation of Privacy Risk for Search Queries : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-48CC-F %I Universität des Saarlandes %C Saarbrücken %D 2015 %P X, 48 p. %V master %9 master
[221]
P. Mandros, “Information Theoretic Supervised Feature Selection for Continuous Data,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{MandrosMaster2015, TITLE = {Information Theoretic Supervised Feature Selection for Continuous Data}, AUTHOR = {Mandros, Panagiotis}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Mandros, Panagiotis %Y Weikum, Gerhard %A referee: Vreeken, Jilles %+ International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Information Theoretic Supervised Feature Selection for Continuous Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-BAF3-F %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 67 p. %V master %9 master
[222]
S. Metzger, R. Schenkel, and M. Sydow, “Aspect-based Similar Entity Search in Semantic Knowledge Graphs with Diversity-awareness and Relaxation,” in The 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops (WI-IAT 2014), Warsaw, Poland, 2015.
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@inproceedings{MetzgerIAT2014, TITLE = {Aspect-based Similar Entity Search in Semantic Knowledge Graphs with Diversity-awareness and Relaxation}, AUTHOR = {Metzger, Steffen and Schenkel, Ralf and Sydow, Marcin}, LANGUAGE = {eng}, ISBN = {978-1-4799-4143-8}, DOI = {10.1109/WI-IAT.2014.17}, PUBLISHER = {IEEE}, YEAR = {2014}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {The 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology -- Workshops (WI-IAT 2014)}, EDITOR = {{\'S}l{\c e}zak, Dominik and Nguyen, Hung Son and Reformat, Marek and Santos, Eugene}, PAGES = {60--69}, ADDRESS = {Warsaw, Poland}, }
Endnote
%0 Conference Proceedings %A Metzger, Steffen %A Schenkel, Ralf %A Sydow, Marcin %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Aspect-based Similar Entity Search in Semantic Knowledge Graphs with Diversity-awareness and Relaxation : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-424D-5 %R 10.1109/WI-IAT.2014.17 %D 2015 %B IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology %Z date of event: 2014-08-11 - 2014-08-14 %C Warsaw, Poland %B The 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops %E Ślęzak, Dominik; Nguyen, Hung Son; Reformat, Marek; Santos, Eugene %P 60 - 69 %I IEEE %@ 978-1-4799-4143-8
[223]
S. Metzler and P. Miettinen, “Join Size Estimation on Boolean Tensors of RDF Data,” in WWW’15 Companion, Florence, Italy, 2015.
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@inproceedings{metzler15join, TITLE = {Join Size Estimation on {Boolean} Tensors of {RDF} Data}, AUTHOR = {Metzler, Saskia and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-4503-3473-0}, DOI = {10.1145/2740908.2742738}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {WWW'15 Companion}, PAGES = {77--78}, ADDRESS = {Florence, Italy}, }
Endnote
%0 Conference Proceedings %A Metzler, Saskia %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Join Size Estimation on Boolean Tensors of RDF Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-CCED-A %R 10.1145/2740908.2742738 %D 2015 %B 24th International Conference on World Wide Web %Z date of event: 2015-05-18 - 2015-05-22 %C Florence, Italy %B WWW'15 Companion %P 77 - 78 %I ACM %@ 978-1-4503-3473-0
[224]
S. Metzler and P. Miettinen, “Clustering Boolean Tensors,” 2015. [Online]. Available: http://arxiv.org/abs/1501.00696. (arXiv: 1501.00696)
Abstract
Tensor factorizations are computationally hard problems, and in particular, are often significantly harder than their matrix counterparts. In case of Boolean tensor factorizations -- where the input tensor and all the factors are required to be binary and we use Boolean algebra -- much of that hardness comes from the possibility of overlapping components. Yet, in many applications we are perfectly happy to partition at least one of the modes. In this paper we investigate what consequences does this partitioning have on the computational complexity of the Boolean tensor factorizations and present a new algorithm for the resulting clustering problem. This algorithm can alternatively be seen as a particularly regularized clustering algorithm that can handle extremely high-dimensional observations. We analyse our algorithms with the goal of maximizing the similarity and argue that this is more meaningful than minimizing the dissimilarity. As a by-product we obtain a PTAS and an efficient 0.828-approximation algorithm for rank-1 binary factorizations. Our algorithm for Boolean tensor clustering achieves high scalability, high similarity, and good generalization to unseen data with both synthetic and real-world data sets.
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@online{metzler15clustering:arxiv, TITLE = {Clustering {Boolean} Tensors}, AUTHOR = {Metzler, Saskia and Miettinen, Pauli}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1501.00696}, EPRINT = {1501.00696}, EPRINTTYPE = {arXiv}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Tensor factorizations are computationally hard problems, and in particular, are often significantly harder than their matrix counterparts. In case of Boolean tensor factorizations -- where the input tensor and all the factors are required to be binary and we use Boolean algebra -- much of that hardness comes from the possibility of overlapping components. Yet, in many applications we are perfectly happy to partition at least one of the modes. In this paper we investigate what consequences does this partitioning have on the computational complexity of the Boolean tensor factorizations and present a new algorithm for the resulting clustering problem. This algorithm can alternatively be seen as a particularly regularized clustering algorithm that can handle extremely high-dimensional observations. We analyse our algorithms with the goal of maximizing the similarity and argue that this is more meaningful than minimizing the dissimilarity. As a by-product we obtain a PTAS and an efficient 0.828-approximation algorithm for rank-1 binary factorizations. Our algorithm for Boolean tensor clustering achieves high scalability, high similarity, and good generalization to unseen data with both synthetic and real-world data sets.}, }
Endnote
%0 Report %A Metzler, Saskia %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Clustering Boolean Tensors : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-6C5B-8 %U http://arxiv.org/abs/1501.00696 %D 2015 %X Tensor factorizations are computationally hard problems, and in particular, are often significantly harder than their matrix counterparts. In case of Boolean tensor factorizations -- where the input tensor and all the factors are required to be binary and we use Boolean algebra -- much of that hardness comes from the possibility of overlapping components. Yet, in many applications we are perfectly happy to partition at least one of the modes. In this paper we investigate what consequences does this partitioning have on the computational complexity of the Boolean tensor factorizations and present a new algorithm for the resulting clustering problem. This algorithm can alternatively be seen as a particularly regularized clustering algorithm that can handle extremely high-dimensional observations. We analyse our algorithms with the goal of maximizing the similarity and argue that this is more meaningful than minimizing the dissimilarity. As a by-product we obtain a PTAS and an efficient 0.828-approximation algorithm for rank-1 binary factorizations. Our algorithm for Boolean tensor clustering achieves high scalability, high similarity, and good generalization to unseen data with both synthetic and real-world data sets. %K Computer Science, Numerical Analysis, cs.NA,Computer Science, Data Structures and Algorithms, cs.DS
[225]
S. Metzler and P. Miettinen, “Clustering Boolean Tensors,” Data Mining and Knowledge Discovery, vol. 29, no. 5, 2015.
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@article{MetzlerMiettinen2015, TITLE = {Clustering {Boolean} tensors}, AUTHOR = {Metzler, Saskia and Miettinen, Pauli}, LANGUAGE = {eng}, DOI = {10.1007/s10618-015-0420-3}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, JOURNAL = {Data Mining and Knowledge Discovery}, VOLUME = {29}, NUMBER = {5}, PAGES = {1343--1373}, }
Endnote
%0 Journal Article %A Metzler, Saskia %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Clustering Boolean Tensors : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-536A-B %R 10.1007/s10618-015-0420-3 %7 2015 %D 2015 %J Data Mining and Knowledge Discovery %V 29 %N 5 %& 1343 %P 1343 - 1373 %I Springer %C New York, NY
[226]
P. Miettinen, “Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: Complexity Beyond Blocks,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, 2015.
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@inproceedings{MiettinenECML2015, TITLE = {Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: {C}omplexity Beyond Blocks}, AUTHOR = {Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-3-319-23524-0}, DOI = {10.1007/978-3-319-23525-7_3}, PUBLISHER = {Springer}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2015)}, EDITOR = {Appice, Annalisa and Pereira Rodrigues, Pedro and Gama, Jo{\~a}o and Al{\'i}pio, Jorge and Soares, Carlos}, PAGES = {36--52}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {9285}, ADDRESS = {Porto, Portugal}, }
Endnote
%0 Conference Proceedings %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: Complexity Beyond Blocks : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-2278-1 %R 10.1007/978-3-319-23525-7_3 %D 2015 %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases %Z date of event: 2015-09-07 - 2015-09-11 %C Porto, Portugal %B Machine Learning and Knowledge Discovery in Databases %E Appice, Annalisa; Pereira Rodrigues, Pedro; Gama, João; Alípio, Jorge; Soares, Carlos %P 36 - 52 %I Springer %@ 978-3-319-23524-0 %B Lecture Notes in Artificial Intelligence %N 9285
[227]
S. Mukherjee, H. Lamba, and G. Weikum, “Experience-aware Item Recommendation in Evolving Review Communities,” in 15th IEEE International Conference on Data Mining (ICDM 2015), Atlantic City, NJ, USA, 2015.
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@inproceedings{mukherjee-experience-model, TITLE = {Experience-aware Item Recommendation in Evolving Review Communities}, AUTHOR = {Mukherjee, Subhabrata and Lamba, Hemank and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4673-9503-8}, DOI = {10.1109/ICDM.2015.111}, PUBLISHER = {IEEE}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {15th IEEE International Conference on Data Mining (ICDM 2015)}, EDITOR = {Aggarwal, Charu and Zhou, Zhi-Hua and Tuzhilin, Alexander and Xiong, Hui and Wu, Xindong}, PAGES = {925--930}, ADDRESS = {Atlantic City, NJ, USA}, }
Endnote
%0 Conference Proceedings %A Mukherjee, Subhabrata %A Lamba, Hemank %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Experience-aware Item Recommendation in Evolving Review Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-49F3-F %R 10.1109/ICDM.2015.111 %D 2015 %B 15th International Conference on Data Mining %Z date of event: 2015-11-14 - 2015-11-17 %C Atlantic City, NJ, USA %B 15th IEEE International Conference on Data Mining %E Aggarwal, Charu; Zhou, Zhi-Hua; Tuzhilin, Alexander; Xiong, Hui; Wu, Xindong %P 925 - 930 %I IEEE %@ 978-1-4673-9503-8
[228]
S. Mukherjee and G. Weikum, “Leveraging Joint Interactions for Credibility Analysis in News Communities,” in CIKM’15, 24th ACM International Conference on Information and Knowledge Management, Melbourne, Australia, 2015.
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@inproceedings{mukherjee-credibility-analysis, TITLE = {Leveraging Joint Interactions for Credibility Analysis in News Communities}, AUTHOR = {Mukherjee, Subhabrata and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-3794-6}, DOI = {10.1145/2806416.2806537}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {CIKM'15, 24th ACM International Conference on Information and Knowledge Management}, PAGES = {353--362}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Mukherjee, Subhabrata %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Leveraging Joint Interactions for Credibility Analysis in News Communities : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-49DE-1 %R 10.1145/2806416.2806537 %D 2015 %B 24th ACM International Conference on Information and Knowledge Management %Z date of event: 2015-10-19 - 2015-10-23 %C Melbourne, Australia %B CIKM'15 %P 353 - 362 %I ACM %@ 978-1-4503-3794-6
[229]
S. Neumann, “On Some Problems of Rounding Rank,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{NeumannMaster2015, TITLE = {On Some Problems of Rounding Rank}, AUTHOR = {Neumann, Stefan}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Neumann, Stefan %Y Miettinen, Pauli %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T On Some Problems of Rounding Rank : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-57D6-2 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P X, 77 p. %V master %9 master
[230]
H.-V. Nguyen and J. Vreeken, “Non-parametric Jensen-Shannon Divergence,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, 2015.
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@inproceedings{NguyenECML2015, TITLE = {Non-parametric {Jensen}-{Shannon} Divergence}, AUTHOR = {Nguyen, Hoang-Vu and Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-3-319-23524-0}, DOI = {10.1007/978-3-319-23525-7_11}, PUBLISHER = {Springer}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2015)}, EDITOR = {Appice, Annalisa and Pereira Rodrigues, Pedro and Gama, Jo{\~a}o and Al{\'i}pio, Jorge and Soares, Carlos}, PAGES = {173--189}, SERIES = {Lecture Notes in Artificial Intelligence}, VOLUME = {9285}, ADDRESS = {Porto, Portugal}, }
Endnote
%0 Conference Proceedings %A Nguyen, Hoang-Vu %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Non-parametric Jensen-Shannon Divergence : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-2286-3 %R 10.1007/978-3-319-23525-7_11 %D 2015 %B European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases %Z date of event: 2015-09-07 - 2015-09-11 %C Porto, Portugal %B Machine Learning and Knowledge Discovery in Databases %E Appice, Annalisa; Pereira Rodrigues, Pedro; Gama, João; Alípio, Jorge; Soares, Carlos %P 173 - 189 %I Springer %@ 978-3-319-23524-0 %B Lecture Notes in Artificial Intelligence %N 9285
[231]
F. Petroni, L. Del Corro, and R. Gemulla, “CORE: Context-Aware Open Relation Extraction with Factorization Machines,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), Lisbon, Portugal, 2015.
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@inproceedings{conf/emnlp/PetroniCG15, TITLE = {{CORE}: {C}ontext-Aware {O}pen {R}elation {E}xtraction with Factorization Machines}, AUTHOR = {Petroni, Fabio and Del Corro, Luciano and Gemulla, Rainer}, LANGUAGE = {eng}, ISBN = {978-1-941643-32-7}, URL = {http://aclweb.org/anthology/D15-1204}, PUBLISHER = {ACL}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)}, PAGES = {1763--1773}, ADDRESS = {Lisbon, Portugal}, }
Endnote
%0 Conference Proceedings %A Petroni, Fabio %A Del Corro, Luciano %A Gemulla, Rainer %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T CORE: Context-Aware Open Relation Extraction with Factorization Machines : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-4112-5 %U http://aclweb.org/anthology/D15-1204 %D 2015 %B Conference on Empirical Methods in Natural Language Processing %Z date of event: 2015-09-17 - 2015-09-21 %C Lisbon, Portugal %B Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing %P 1763 - 1773 %I ACL %@ 978-1-941643-32-7 %U http://aclweb.org/anthology/D/D15/D15-1204.pdf
[232]
R. Pienta, Z. Lin, M. Kahng, J. Vreeken, P. P. Talukdar, J. Abello, G. Parameswaran, and D. H. Chau, “AdaptiveNav: Adaptive Discovery of Interesting and Surprising Nodes in Large Graphs,” in IEEE VIS 2015, Chicago, IL, USA, 2015.
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@inproceedings{pienta:15:adaptivenav, TITLE = {{AdaptiveNav}: {A}daptive Discovery of Interesting and Surprising Nodes in Large Graphs}, AUTHOR = {Pienta, Robert and Lin, Zhiyuan and Kahng, Minsuk and Vreeken, Jilles and Talukdar, Partha P. and Abello, James and Parameswaran, Ganesh and Chau, Duen Horng}, LANGUAGE = {eng}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE VIS 2015}, ADDRESS = {Chicago, IL, USA}, }
Endnote
%0 Conference Proceedings %A Pienta, Robert %A Lin, Zhiyuan %A Kahng, Minsuk %A Vreeken, Jilles %A Talukdar, Partha P. %A Abello, James %A Parameswaran, Ganesh %A Chau, Duen Horng %+ External Organizations External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T AdaptiveNav: Adaptive Discovery of Interesting and Surprising Nodes in Large Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-57B4-E %D 2015 %B IEEE VIS 2015 %Z date of event: 2015-10-25 - 2015-10-30 %C Chicago, IL, USA %B IEEE VIS 2015
[233]
N. Prytkova, M. Spaniol, and G. Weikum, “Aligning Multi-cultural Knowledge Taxonomies by Combinatorial Optimization,” in WWW’15 Companion, Florence, Italy, 2015.
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@inproceedings{PSWe15, TITLE = {Aligning Multi-cultural Knowledge Taxonomies by Combinatorial Optimization}, AUTHOR = {Prytkova, Natalia and Spaniol, Marc and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-3473-0}, DOI = {10.1145/2740908.2742721}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {WWW'15 Companion}, PAGES = {93--94}, ADDRESS = {Florence, Italy}, }
Endnote
%0 Conference Proceedings %A Prytkova, Natalia %A Spaniol, Marc %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Aligning Multi-cultural Knowledge Taxonomies by Combinatorial Optimization : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0025-06E5-3 %R 10.1145/2740908.2742721 %D 2015 %B 24th International Conference on World Wide Web %Z date of event: 2015-05-18 - 2015-05-22 %C Florence, Italy %B WWW'15 Companion %P 93 - 94 %I ACM %@ 978-1-4503-3473-0
[234]
A. Rohrbach, M. Rohrbach, N. Tandon, and B. Schiele, “A Dataset for Movie Description,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, MA, USA, 2015.
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@inproceedings{Rohrbach15cvpr, TITLE = {A Dataset for Movie Description}, AUTHOR = {Rohrbach, Anna and Rohrbach, Marcus and Tandon, Niket and Schiele, Bernt}, LANGUAGE = {eng}, DOI = {10.1109/CVPR.2015.7298940}, PUBLISHER = {IEEE}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)}, PAGES = {3202--3212}, ADDRESS = {Boston, MA, USA}, }
Endnote
%0 Conference Proceedings %A Rohrbach, Anna %A Rohrbach, Marcus %A Tandon, Niket %A Schiele, Bernt %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T A Dataset for Movie Description : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0025-01B9-B %R 10.1109/CVPR.2015.7298940 %D 2015 %B IEEE Conference on Computer Vision and Pattern Recognition %Z date of event: 2015-06-08 - 2015-06-10 %C Boston, MA, USA %B IEEE Conference on Computer Vision and Pattern Recognition %P 3202 - 3212 %I IEEE
[235]
C. Schulte, B. Taneva, and G. Weikum, “On-topic Cover Stories from News Archives,” in Advances in Information Retrieval (ECIR 2015), Vienna, Austria, 2015, pp. 37–42.
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@inproceedings{Schulte:ECIR2015, TITLE = {On-topic Cover Stories from News Archives}, AUTHOR = {Schulte, Christian and Taneva, Bilyana and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-3-319-16353-6}, DOI = {10.1007/978-3-319-16354-3_4}, PUBLISHER = {Springer}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2015)}, EDITOR = {Hanbury, Allan and Kazai, Gabriella and Rauber, Andreas and Fuhr, Norbert}, PAGES = {37--42}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {9022}, ADDRESS = {Vienna, Austria}, }
Endnote
%0 Conference Proceedings %A Schulte, Christian %A Taneva, Bilyana %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T On-topic Cover Stories from News Archives : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-A6DE-B %R 10.1007/978-3-319-16354-3_4 %D 2015 %B 37th European Conference on Information Retrieval %Z date of event: 2015-03-29 - 2015-04-02 %C Vienna, Austria %B Advances in Information Retrieval %E Hanbury, Allan; Kazai, Gabriella; Rauber, Andreas; Fuhr, Norbert %P 37 - 42 %I Springer %@ 978-3-319-16353-6 %B Lecture Notes in Computer Science %N 9022
[236]
S. Seufert, “Algorithmic Building Blocks for Relationship Analysis over Large Graphs,” Universität des Saarlandes, Saarbrücken, 2015.
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@phdthesis{Seufertphd15, TITLE = {Algorithmic Building Blocks for Relationship Analysis over Large Graphs}, AUTHOR = {Seufert, Stephan}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Seufert, Stephan %Y Bedathur, Srikanta %A referee: Barbosa, Denilson %A referee: Weidenbach, Christoph %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Automation of Logic, MPI for Informatics, Max Planck Society %T Algorithmic Building Blocks for Relationship Analysis over Large Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-6E65-D %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 198 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/volltexte/2015/6183/http://scidok.sulb.uni-saarland.de/doku/urheberrecht.php?la=de
[237]
D. Seyler, M. Yahya, and K. Berberich, “Generating Quiz Questions from Knowledge Graphs,” in WWW’15 Companion, Florence, Italy, 2015.
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@inproceedings{SeylerWWW2015, TITLE = {Generating Quiz Questions from Knowledge Graphs}, AUTHOR = {Seyler, Dominic and Yahya, Mohamed and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-3473-0}, DOI = {10.1145/2740908.2742722}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {WWW'15 Companion}, PAGES = {113--114}, ADDRESS = {Florence, Italy}, }
Endnote
%0 Conference Proceedings %A Seyler, Dominic %A Yahya, Mohamed %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Generating Quiz Questions from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-E33C-4 %R 10.1145/2740908.2742722 %D 2015 %B 24th International Conference on World Wide Web %Z date of event: 2015-04-18 - 2015-04-22 %C Florence, Italy %B WWW'15 Companion %P 113 - 114 %I ACM %@ 978-1-4503-3473-0
[238]
D. Seyler, “Question Generation from Knowledge Graphs,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{SeylerMaster2015, TITLE = {Question Generation from Knowledge Graphs}, AUTHOR = {Seyler, Dominic}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Seyler, Dominic %Y Berberich, Klaus %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Question Generation from Knowledge Graphs : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-08B0-4 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P XII, 104 p. %V master %9 master
[239]
E. Shutova, N. Tandon, and G. de Melo, “Perceptually Grounded Selectional Preferences,” in The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL 2015), Beijing, China, 2015.
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@inproceedings{ShutovaTandonDemelo:ACL2015, TITLE = {Perceptually Grounded Selectional Preferences}, AUTHOR = {Shutova, Ekaterina and Tandon, Niket and de Melo, Gerard}, LANGUAGE = {eng}, ISBN = {978-1-941643-72-3}, URL = {http://www.aclweb.org/anthology/P15-1092}, PUBLISHER = {ACL}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL 2015)}, PAGES = {950--960}, ADDRESS = {Beijing, China}, }
Endnote
%0 Conference Proceedings %A Shutova, Ekaterina %A Tandon, Niket %A de Melo, Gerard %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Perceptually Grounded Selectional Preferences : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-54B8-9 %U http://www.aclweb.org/anthology/P15-1092 %D 2015 %Z Review method: peer-reviewed %B 53rd Annual Meeting of the Association for Computational Linguistics %Z date of event: 2015-07-26 - 2015-07-31 %C Beijing, China %B The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing %P 950 - 960 %I ACL %@ 978-1-941643-72-3 %U http://www.aclweb.org/anthology/P/P15/P15-1092.pdf
[240]
A. Sierra, “Ad-hoc Information Retrieval using Annotated Queries and Documents,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{SierraMaster2015, TITLE = {Ad-hoc Information Retrieval using Annotated Queries and Documents}, AUTHOR = {Sierra, Alejandro}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Sierra, Alejandro %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Ad-hoc Information Retrieval using Annotated Queries and Documents : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0025-A968-D %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 68 p. %V master %9 master
[241]
A. Siu and G. Weikum, “Semantic Type Classification of Common Words in Biomedical Noun Phrases,” in Workshop on Biomedical Natural Language Processing (BioNLP 2015), Beijing, China, 2015.
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@inproceedings{Siu15, TITLE = {Semantic Type Classification of Common Words in Biomedical Noun Phrases}, AUTHOR = {Siu, Amy and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-5108-0943-7}, PUBLISHER = {ACL}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Workshop on Biomedical Natural Language Processing (BioNLP 2015)}, PAGES = {98--103}, ADDRESS = {Beijing, China}, }
Endnote
%0 Conference Proceedings %A Siu, Amy %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Semantic Type Classification of Common Words in Biomedical Noun Phrases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-2042-0 %D 2015 %B Workshop on Biomedical Natural Language Processing %Z date of event: 2015-07-30 - 2015-07-30 %C Beijing, China %B Workshop on Biomedical Natural Language Processing %P 98 - 103 %I ACL %@ 978-1-5108-0943-7
[242]
M. Srinivasamurthy, “Mining European Statistics for Social Events,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{SrinivasamurthyMSc2015, TITLE = {Mining European Statistics for Social Events}, AUTHOR = {Srinivasamurthy, Mena}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Srinivasamurthy, Mena %Y Weikum, Gerhard %A referee: Spaniol, Marc %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mining European Statistics for Social Events : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-43AB-1 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 52 p. %V master %9 master
[243]
S. Sundareisan, J. Vreeken, and B. A. Prakash, “Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2015), Vancouver, Canada, 2015.
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@inproceedings{sundareisan:15:netfill, TITLE = {Hidden Hazards: {Finding} Missing Nodes in Large Graph Epidemics}, AUTHOR = {Sundareisan, Shashi and Vreeken, Jilles and Prakash, B. Aditya}, LANGUAGE = {eng}, ISBN = {978-1-61197-401-0}, DOI = {10.1137/1.9781611974010.47}, PUBLISHER = {SIAM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2015)}, EDITOR = {Venkatasubramanian, Suresh and Ye, Jieping}, PAGES = {415--423}, ADDRESS = {Vancouver, Canada}, }
Endnote
%0 Conference Proceedings %A Sundareisan, Shashi %A Vreeken, Jilles %A Prakash, B. Aditya %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-A82A-2 %R 10.1137/1.9781611974010.47 %D 2015 %B 15th SIAM International Conference on Data Mining %Z date of event: 2015-04-30 - 2015-05-02 %C Vancouver, Canada %B Proceedings of the SIAM International Conference on Data Mining %E Venkatasubramanian, Suresh; Ye, Jieping %P 415 - 423 %I SIAM %@ 978-1-61197-401-0
[244]
N. Tandon, G. de Melo, A. De, and G. Weikum, “Lights, Camera, Action: Knowledge Extraction from Movie Scripts,” in WWW’15 Companion, Florence, Italy, 2015.
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@inproceedings{tandon2015moviescripts, TITLE = {Lights, Camera, Action: Knowledge Extraction from Movie Scripts}, AUTHOR = {Tandon, Niket and de Melo, Gerard and De, Abir and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-3473-0}, DOI = {10.1145/2740908.2742756}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {WWW'15 Companion}, PAGES = {127--128}, ADDRESS = {Florence, Italy}, }
Endnote
%0 Conference Proceedings %A Tandon, Niket %A de Melo, Gerard %A De, Abir %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Lights, Camera, Action: Knowledge Extraction from Movie Scripts : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-E32D-6 %R 10.1145/2740908.2742756 %D 2015 %B 24th International Conference on World Wide Web %Z date of event: 2015-05-18 - 2015-05-22 %C Florence, Italy %B WWW'15 Companion %P 127 - 128 %I ACM %@ 978-1-4503-3473-0
[245]
N. Tandon, G. de Melo, A. De, and G. Weikum, “Knowlywood: Mining Activity Knowledge From Hollywood Narratives,” in CIKM’15, 24th ACM International Conference on Information and Knowledge Management, Melbourne, Australia, 2015.
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@inproceedings{Tandon:2015:KMA:2806416.2806583, TITLE = {Knowlywood: {M}ining Activity Knowledge From {H}ollywood Narratives}, AUTHOR = {Tandon, Niket and de Melo, Gerard and De, Abir and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-3794-6}, DOI = {10.1145/2806416.2806583}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {CIKM'15, 24th ACM International Conference on Information and Knowledge Management}, PAGES = {223--232}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Tandon, Niket %A de Melo, Gerard %A De, Abir %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T Knowlywood: Mining Activity Knowledge From Hollywood Narratives : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-49E0-A %R 10.1145/2806416.2806583 %D 2015 %B 24th ACM International Conference on Information and Knowledge Management %Z date of event: 2015-10-19 - 2015-10-23 %C Melbourne, Australia %B CIKM'15 %P 223 - 232 %I ACM %@ 978-1-4503-3794-6
[246]
C. Teflioudi, R. Gemulla, and O. Mykytiuk, “LEMP: Fast Retrieval of Large Entries in a Matrix Product,” in SIGMOD’15, ACM SIGMOD International Conference on Management of Data, Melbourne, Australia, 2015.
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@inproceedings{Teflioudi15, TITLE = {{LEMP}: {F}ast Retrieval of Large Entries in a Matrix Product}, AUTHOR = {Teflioudi, Christina and Gemulla, Rainer and Mykytiuk, Olga}, LANGUAGE = {eng}, ISBN = {978-1-4503-2758-9}, DOI = {10.1145/2723372.2747647}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {SIGMOD'15, ACM SIGMOD International Conference on Management of Data}, PAGES = {107--122}, ADDRESS = {Melbourne, Australia}, }
Endnote
%0 Conference Proceedings %A Teflioudi, Christina %A Gemulla, Rainer %A Mykytiuk, Olga %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T LEMP: Fast Retrieval of Large Entries in a Matrix Product : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-4A1C-F %R 10.1145/2723372.2747647 %D 2015 %B ACM SIGMOD International Conference on Management of Data %Z date of event: 2015-05-31 - 2015-06-04 %C Melbourne, Australia %B SIGMOD'15 %P 107 - 122 %I ACM %@ 978-1-4503-2758-9
[247]
C. Tryfonopoulos, P. Raftopoulou, V. Setty, and A. Xiros, “Towards Content-Based Publish/Subscribe for Distributed Social Networks,” in DEBS’15, 9th ACM International Conference on Distributed Event-Based Systems, Oslo, Norway, 2015.
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@inproceedings{DBLP:conf/debs/TryfonopoulosRS15, TITLE = {Towards Content-Based Publish/Subscribe for Distributed Social Networks}, AUTHOR = {Tryfonopoulos, Christos and Raftopoulou, Paraskevi and Setty, Vinay and Xiros, Argiris}, LANGUAGE = {eng}, ISBN = {978-1-4503-3286-6}, DOI = {10.1145/2675743.2776770}, PUBLISHER = {ACM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {DEBS'15, 9th ACM International Conference on Distributed Event-Based Systems}, PAGES = {340--343}, ADDRESS = {Oslo, Norway}, }
Endnote
%0 Conference Proceedings %A Tryfonopoulos, Christos %A Raftopoulou, Paraskevi %A Setty, Vinay %A Xiros, Argiris %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Towards Content-Based Publish/Subscribe for Distributed Social Networks : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002C-1CB1-8 %R 10.1145/2675743.2776770 %D 2015 %B 9th ACM International Conference on Distributed Event-Based Systems %Z date of event: 2015-06-29 - 2015-07-03 %C Oslo, Norway %B DEBS'15 %P 340 - 343 %I ACM %@ 978-1-4503-3286-6
[248]
T. Tylenda, “Methods and Tools for Summarization of Entities and Facts in Knowledge Bases,” Universität des Saarlandes, Saarbrücken, 2015.
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@phdthesis{TylendaPhd15, TITLE = {Methods and Tools for Summarization of Entities and Facts in Knowledge Bases}, AUTHOR = {Tylenda, Tomasz}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Tylenda, Tomasz %Y Weikum, Gerhard %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Methods and Tools for Summarization of Entities and Facts in Knowledge Bases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0028-FC65-5 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P 113 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/volltexte/2015/6263/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[249]
J. Vreeken, “Causal Inference by Direction of Information,” in Proceedings of the SIAM International Conference on Data Mining (SDM 2015), Vancouver, Canada, 2015.
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@inproceedings{vreeken:15:ergo, TITLE = {Causal Inference by Direction of Information}, AUTHOR = {Vreeken, Jilles}, LANGUAGE = {eng}, ISBN = {978-1-61197-401-0}, DOI = {10.1137/1.9781611974010.102}, PUBLISHER = {SIAM}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM 2015)}, EDITOR = {Venkatasubramanian, Suresh and Ye, Jieping}, PAGES = {909--917}, ADDRESS = {Vancouver, Canada}, }
Endnote
%0 Conference Proceedings %A Vreeken, Jilles %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Causal Inference by Direction of Information : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-A82C-D %R 10.1137/1.9781611974010.102 %D 2015 %B 15th SIAM International Conference on Data Mining %Z date of event: 2015-04-30 - 2015-05-02 %C Vancouver, Canada %B Proceedings of the SIAM International Conference on Data Mining %E Venkatasubramanian, Suresh; Ye, Jieping %P 909 - 917 %I SIAM %@ 978-1-61197-401-0
[250]
H. Wang, “Retrospective Summarization: What Did I Miss?,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{WangMaster2015, TITLE = {Retrospective Summarization: What Did I Miss?}, AUTHOR = {Wang, He}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Wang, He %Y Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Retrospective Summarization: What Did I Miss? : %U http://hdl.handle.net/11858/00-001M-0000-0026-A0B4-B %I Universität des Saarlandes %C Saarbrücken %D 2015 %P XVI, 73 p. %V master %9 master
[251]
M. A. Yosef, “U-AIDA: A Customizable System for Named Entity Recognition, Classification, and Disambiguation,” Universität des Saarlandes, Saarbrücken, 2015.
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@phdthesis{Yosefphd15, TITLE = {U-{AIDA}: A Customizable System for Named Entity Recognition, Classification, and Disambiguation}, AUTHOR = {Yosef, Mohamed Amir}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Yosef, Mohamed Amir %Y Weikum, Gerhard %A referee: Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T U-AIDA: A Customizable System for Named Entity Recognition, Classification, and Disambiguation : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-B9B9-C %I Universität des Saarlandes %C Saarbrücken %D 2015 %P XV, 101 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/volltexte/2016/6370/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[252]
A. Zimek and J. Vreeken, “The Blind Men and the Elephant: On Meeting the Problem of Multiple Truths in Data from Clustering and Pattern Mining Perspectives,” Machine Learning, vol. 98, no. 1, 2015.
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@article{zimek:15:blind, TITLE = {The Blind Men and the Elephant: On Meeting the Problem of Multiple Truths in Data from Clustering and Pattern Mining Perspectives}, AUTHOR = {Zimek, Arthur and Vreeken, Jilles}, LANGUAGE = {eng}, ISSN = {0885-6125}, DOI = {10.1007/s10994-013-5334-y}, PUBLISHER = {Springer}, ADDRESS = {New York, NY}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, JOURNAL = {Machine Learning}, VOLUME = {98}, NUMBER = {1}, PAGES = {121--155}, }
Endnote
%0 Journal Article %A Zimek, Arthur %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society %T The Blind Men and the Elephant: On Meeting the Problem of Multiple Truths in Data from Clustering and Pattern Mining Perspectives : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-57AE-D %R 10.1007/s10994-013-5334-y %7 2013-03-07 %D 2015 %J Machine Learning %V 98 %N 1 %& 121 %P 121 - 155 %I Springer %C New York, NY %@ false
[253]
T. Zinchenko, “Redescription Mining Over non-Binary Data Sets Using Decision Trees,” Universität des Saarlandes, Saarbrücken, 2015.
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@mastersthesis{ZinchenkoMaster2014, TITLE = {Redescription Mining Over non-Binary Data Sets Using Decision Trees}, AUTHOR = {Zinchenko, Tetiana}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, }
Endnote
%0 Thesis %A Zinchenko, Tetiana %Y Miettinen, Pauli %A referee: Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Redescription Mining Over non-Binary Data Sets Using Decision Trees : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-B73A-5 %I Universität des Saarlandes %C Saarbrücken %D 2015 %P X, 118 p. %V master %9 master
[254]
T. Zinchenko, E. Galbrun, and P. Miettinen, “Mining Predictive Redescriptions with Trees,” in 15th IEEE International Conference on Data Mining Workshop (ICDMW 2015), Atlantic City, NJ, USA, 2015.
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@inproceedings{zinchenko15mining, TITLE = {Mining Predictive Redescriptions with Trees}, AUTHOR = {Zinchenko, Tetiana and Galbrun, Esther and Miettinen, Pauli}, LANGUAGE = {eng}, ISBN = {978-1-4673-8492-6}, DOI = {10.1109/ICDMW.2015.123}, PUBLISHER = {IEEE}, YEAR = {2015}, MARGINALMARK = {$\bullet$}, DATE = {2015}, BOOKTITLE = {15th IEEE International Conference on Data Mining Workshop (ICDMW 2015)}, EDITOR = {Cui, Peng and Dy, Jennifer and Aggarwal, Charu and Zhou, Zhi-Hua and Tuzhilin, Alexander and Xiong, Hui and Wu, Xindong}, PAGES = {1672--1675}, ADDRESS = {Atlantic City, NJ, USA}, }
Endnote
%0 Conference Proceedings %A Zinchenko, Tetiana %A Galbrun, Esther %A Miettinen, Pauli %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Mining Predictive Redescriptions with Trees : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-5424-A %R 10.1109/ICDMW.2015.123 %D 2015 %B 15th International Conference on Data Mining %Z date of event: 2015-11-14 - 2015-11-17 %C Atlantic City, NJ, USA %B 15th IEEE International Conference on Data Mining Workshop %E Cui, Peng; Dy, Jennifer; Aggarwal, Charu; Zhou, Zhi-Hua; Tuzhilin, Alexander; Xiong, Hui; Wu, Xindong %P 1672 - 1675 %I IEEE %@ 978-1-4673-8492-6
2014
[255]
F. Alvanaki, “Mining Interesting Events on Large and Dynamic Data,” Universität des Saarlandes, Saarbrücken, 2014.
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@phdthesis{Alvanakithesis, TITLE = {Mining Interesting Events on Large and Dynamic Data}, AUTHOR = {Alvanaki, Foteini}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2014}, DATE = {2014}, }
Endnote
%0 Thesis %A Alvanaki, Foteini %Y Michel, Sebastian %A referee: Weikum, Gerhard %A referee: Delis, Alexis %+ Databases and Information Systems, MPI for Informatics, Max Planck Society International Max Planck Research School, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Mining Interesting Events on Large and Dynamic Data : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0025-6C4E-B %I Universität des Saarlandes %C Saarbrücken %D 2014 %P 128 p. %V phd %9 phd %U http://scidok.sulb.uni-saarland.de/volltexte/2015/5985/http://scidok.sulb.uni-saarland.de/doku/lic_ohne_pod.php?la=de
[256]
F. Alvanaki and S. Michel, “Tracking Set Correlations at Large Scale,” in SIGMOD’14, ACM SIGMOD International Conference on Management of Data, Snowbird, UT, USA, 2014.
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@inproceedings{Alvanaki2014, TITLE = {Tracking Set Correlations at Large Scale}, AUTHOR = {Alvanaki, Foteini and Michel, Sebastian}, LANGUAGE = {eng}, ISBN = {978-1-4503-2376-5}, DOI = {10.1145/2588555.2610510}, PUBLISHER = {ACM}, YEAR = {2014}, DATE = {2014}, BOOKTITLE = {SIGMOD'14, ACM SIGMOD International Conference on Management of Data}, EDITOR = {Dyresson, Curtis and Li, Feifei and {\"O}zsu, M. Tamer}, PAGES = {1507--1518}, ADDRESS = {Snowbird, UT, USA}, }
Endnote
%0 Conference Proceedings %A Alvanaki, Foteini %A Michel, Sebastian %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Tracking Set Correlations at Large Scale : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0019-8423-2 %R 10.1145/2588555.2610510 %D 2014 %B ACM SIGMOD International Conference on Management of Data %Z date of event: 2014-06-22 - 2014-06-27 %C Snowbird, UT, USA %B SIGMOD'14 %E Dyresson, Curtis; Li, Feifei; Özsu, M. Tamer %P 1507 - 1518 %I ACM %@ 978-1-4503-2376-5
[257]
A. Anand, I. Mele, S. Bedathur, and K. Berberich, “Phrase Query Optimization on Inverted Indexes,” Max-Planck-Institut für Informatik, Saarbrücken, MPI-I-2014-5-002, 2014.
Abstract
Phrase queries are a key functionality of modern search engines. Beyond that, they increasingly serve as an important building block for applications such as entity-oriented search, text analytics, and plagiarism detection. Processing phrase queries is costly, though, since positional information has to be kept in the index and all words, including stopwords, need to be considered. We consider an augmented inverted index that indexes selected variable-length multi-word sequences in addition to single words. We study how arbitrary phrase queries can be processed efficiently on such an augmented inverted index. We show that the underlying optimization problem is NP-hard in the general case and describe an exact exponential algorithm and an approximation algorithm to its solution. Experiments on ClueWeb09 and The New York Times with different real-world query workloads examine the practical performance of our methods.
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@techreport{AnandMeleBedathurBerberich2014, TITLE = {Phrase Query Optimization on Inverted Indexes}, AUTHOR = {Anand, Avishek and Mele, Ida and Bedathur, Srikanta and Berberich, Klaus}, LANGUAGE = {eng}, ISSN = {0946-011X}, NUMBER = {MPI-I-2014-5-002}, INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2014}, ABSTRACT = {Phrase queries are a key functionality of modern search engines. Beyond that, they increasingly serve as an important building block for applications such as entity-oriented search, text analytics, and plagiarism detection. Processing phrase queries is costly, though, since positional information has to be kept in the index and all words, including stopwords, need to be considered. We consider an augmented inverted index that indexes selected variable-length multi-word sequences in addition to single words. We study how arbitrary phrase queries can be processed efficiently on such an augmented inverted index. We show that the underlying optimization problem is NP-hard in the general case and describe an exact exponential algorithm and an approximation algorithm to its solution. Experiments on ClueWeb09 and The New York Times with different real-world query workloads examine the practical performance of our methods.}, TYPE = {Research Report}, }
Endnote
%0 Report %A Anand, Avishek %A Mele, Ida %A Bedathur, Srikanta %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Phrase Query Optimization on Inverted Indexes : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-022A-3 %Y Max-Planck-Institut für Informatik %C Saarbrücken %D 2014 %P 20 p. %X Phrase queries are a key functionality of modern search engines. Beyond that, they increasingly serve as an important building block for applications such as entity-oriented search, text analytics, and plagiarism detection. Processing phrase queries is costly, though, since positional information has to be kept in the index and all words, including stopwords, need to be considered. We consider an augmented inverted index that indexes selected variable-length multi-word sequences in addition to single words. We study how arbitrary phrase queries can be processed efficiently on such an augmented inverted index. We show that the underlying optimization problem is NP-hard in the general case and describe an exact exponential algorithm and an approximation algorithm to its solution. Experiments on ClueWeb09 and The New York Times with different real-world query workloads examine the practical performance of our methods. %B Research Report %@ false
[258]
A. Anand, I. Mele, S. Bedathur, and K. Berberich, “Phrase Query Optimization on Inverted Indexes,” in CIKM’14, 23rd ACM International Conference on Information and Knowledge Management, Shanghai, China, 2014.
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@inproceedings{Anand:CIKM2014, TITLE = {Phrase Query Optimization on Inverted Indexes}, AUTHOR = {Anand, Avishek and Mele, Ida and Bedathur, Srikanta and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4503-2598-1}, DOI = {10.1145/2661829.2661928}, PUBLISHER = {ACM}, YEAR = {2014}, DATE = {2014}, BOOKTITLE = {CIKM'14, 23rd ACM International Conference on Information and Knowledge Management}, EDITOR = {Li, Jianzhong and Wang, X. Sean and Garofalakis, Minos and Soboroff, Ian and Suel, Torsten and Wang, Min}, PAGES = {1807--1810}, ADDRESS = {Shanghai, China}, }
Endnote
%0 Conference Proceedings %A Anand, Avishek %A Mele, Ida %A Bedathur, Srikanta %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Phrase Query Optimization on Inverted Indexes : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-549A-0 %R 10.1145/2661829.2661928 %D 2014 %B 23rd ACM International Conference on Information and Knowledge Management %Z date of event: 2014-11-03 - 2014-11-07 %C Shanghai, China %K multi-word indexing, phrase queries, query optimization %B CIKM'14 %E Li, Jianzhong; Wang, X. Sean; Garofalakis, Minos; Soboroff, Ian; Suel, Torsten; Wang, Min %P 1807 - 1810 %I ACM %@ 978-1-4503-2598-1
[259]
N. An, L. Jiang, J. Wang, P. Luo, M. Wang, and B. N. Li, “Toward Detection of Aliases without String Similarity,” Information Sciences, vol. 261, 2014.
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@article{AnJiangWang2014, TITLE = {Toward Detection of Aliases without String Similarity}, AUTHOR = {An, Ning and Jiang, Lili and Wang, Jianyong and Luo, Ping and Wang, Min and Li, Bing Nan}, LANGUAGE = {eng}, ISSN = {0020-0255}, DOI = {10.1016/j.ins.2013.11.010}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2014}, DATE = {2014}, JOURNAL = {Information Sciences}, VOLUME = {261}, PAGES = {89--100}, }
Endnote
%0 Journal Article %A An, Ning %A Jiang, Lili %A Wang, Jianyong %A Luo, Ping %A Wang, Min %A Li, Bing Nan %+ external Databases and Information Systems, MPI for Informatics, Max Planck Society external external external external %T Toward Detection of Aliases without String Similarity : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-3DFB-8 %F ISI: 000331689700005 %R 10.1016/j.ins.2013.11.010 %7 2013-11-18 %D 2014 %J Information Sciences %O Inf. Sci. %V 261 %& 89 %P 89 - 100 %I Elsevier %C Amsterdam %@ false
[260]
K. Athukorala, A. Oulasvirta, D. Glowacka, J. Vreeken, and G. Jaccuci, “Interaction Model to Predict Subjective-specificity of Search Results,” in UMAP 2014 Extended Proceedings, Aalborg, Denmark, 2014.
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@inproceedings{atukorala:14:interaction, TITLE = {Interaction Model to Predict Subjective-specificity of Search Results}, AUTHOR = {Athukorala, Kumaripaba and Oulasvirta, Antti and Glowacka, Dorata and Vreeken, Jilles and Jaccuci, Giulio}, LANGUAGE = {eng}, URL = {http://ceur-ws.org/Vol-1181/umap2014_lateresults_01.pdf; urn:nbn:de:0074-1181-4}, PUBLISHER = {CEUR-WS.org}, YEAR = {2014}, BOOKTITLE = {UMAP 2014 Extended Proceedings}, EDITOR = {Cantador, Iv{\'a}n and Chi, Min and Farzan, Rosta and J{\"a}schke, Robert}, PAGES = {69--74}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {1181}, ADDRESS = {Aalborg, Denmark}, }
Endnote
%0 Conference Proceedings %A Athukorala, Kumaripaba %A Oulasvirta, Antti %A Glowacka, Dorata %A Vreeken, Jilles %A Jaccuci, Giulio %+ External Organizations Computer Graphics, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Interaction Model to Predict Subjective-specificity of Search Results : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-5397-D %U http://ceur-ws.org/Vol-1181/umap2014_lateresults_01.pdf %D 2014 %B 22nd Conference on User Modeling, Adaptation, and Personalization %Z date of event: 2014-07-07 - 2014-07-11 %C Aalborg, Denmark %B UMAP 2014 Extended Proceedings %E Cantador, Iván; Chi, Min; Farzan, Rosta; Jäschke, Robert %P 69 - 74 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 1181 %U http://ceur-ws.org/Vol-1181/umap2014_lateresults_01.pdf
[261]
K. Athukorala, A. Oulasvirta, D. Glowacka, J. Vreeken, and G. Jaccuci, “Supporting Exploratory Search Through User Modelling,” in UMAP 2014 Extended Proceedings (PIA 2014 in conjunction with UMAP 2014), Aalborg, Denmark, 2014.
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@inproceedings{atukorala:14:supporting, TITLE = {Supporting Exploratory Search Through User Modelling}, AUTHOR = {Athukorala, Kumaripaba and Oulasvirta, Antti and Glowacka, Dorata and Vreeken, Jilles and Jaccuci, Giulio}, LANGUAGE = {eng}, ISSN = {1613-0073}, URL = {http://ceur-ws.org/Vol-1181/pia2014_paper_04.pdf; urn:nbn:de:0074-1181-4; http://ceur-ws.org/Vol-1181/pia2014_proceedings.pdf}, PUBLISHER = {CEUR-WS.org}, YEAR = {2014}, BOOKTITLE = {UMAP 2014 Extended Proceedings (PIA 2014 in conjunction with UMAP 2014)}, EDITOR = {Cantador, Iv{\'a}n and Chi, Min and Farzan, Rosta and J{\"a}schke, Robert}, PAGES = {1--47}, SERIES = {CEUR Workshop Proceedings}, VOLUME = {1181}, ADDRESS = {Aalborg, Denmark}, }
Endnote
%0 Conference Proceedings %A Athukorala, Kumaripaba %A Oulasvirta, Antti %A Glowacka, Dorata %A Vreeken, Jilles %A Jaccuci, Giulio %+ External Organizations Computer Graphics, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Supporting Exploratory Search Through User Modelling : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-538C-7 %U http://ceur-ws.org/Vol-1181/pia2014_paper_04.pdf %D 2014 %B Joint Workshop on Personalised Information Access %Z date of event: 2014-07-07 - 2014-07-07 %C Aalborg, Denmark %B UMAP 2014 Extended Proceedings %E Cantador, Iván; Chi, Min; Farzan, Rosta; Jäschke, Robert %P 1 - 47 %I CEUR-WS.org %B CEUR Workshop Proceedings %N 1181 %@ false %U http://ceur-ws.org/Vol-1181/pia2014_paper_04.pdf
[262]
K. Athukorala, A. Oulasvirta, D. Glowacka, J. Vreeken, and G. Jaccuci, “Narrow or Broad? Estimating Subjective Specificity in Exploratory Search,” in CIKM’14, 23rd ACM International Conference on Information and Knowledge Management, Shanghai, China, 2014.
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@inproceedings{atukorala:14:foraging, TITLE = {Narrow or Broad? {Estimating} Subjective Specificity in Exploratory Search}, AUTHOR = {Athukorala, Kumaripaba and Oulasvirta, Antti and Glowacka, Dorata and Vreeken, Jilles and Jaccuci, Giulio}, LANGUAGE = {eng}, ISBN = {978-1-4503-2598-1}, DOI = {10.1145/2661829.2661904}, PUBLISHER = {ACM}, YEAR = {2014}, DATE = {2014}, BOOKTITLE = {CIKM'14, 23rd ACM International Conference on Information and Knowledge Management}, EDITOR = {Li, Jianzhong and Wang, X. Sean and Garofalakis, Minos and Soboroff, Ian and Suel, Torsten and Wang, Min}, PAGES = {819--828}, ADDRESS = {Shanghai, China}, }
Endnote
%0 Conference Proceedings %A Athukorala, Kumaripaba %A Oulasvirta, Antti %A Glowacka, Dorata %A Vreeken, Jilles %A Jaccuci, Giulio %+ External Organizations Computer Graphics, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations %T Narrow or Broad? Estimating Subjective Specificity in Exploratory Search : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-53A1-6 %R 10.1145/2661829.2661904 %D 2014 %B 23rd ACM International Conference on Information and Knowledge Management %Z date of event: 2014-11-03 - 2014-11-07 %C Shanghai, China %B CIKM'14 %E Li, Jianzhong; Wang, X. Sean; Garofalakis, Minos; Soboroff, Ian; Suel, Torsten; Wang, Min %P 819 - 828 %I ACM %@ 978-1-4503-2598-1
[263]
K. Berberich, “Web Archives,” in Encyclopedia of Social Network Analysis and Mining, Berlin: Springer, 2014.
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@incollection{DBLP:reference/snam/Berberich14, TITLE = {Web Archives}, AUTHOR = {Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-1-4614-6169-2}, DOI = {10.1007/978-1-4614-6170-8_128}, PUBLISHER = {Springer}, ADDRESS = {Berlin}, YEAR = {2014}, DATE = {2014}, BOOKTITLE = {Encyclopedia of Social Network Analysis and Mining}, PAGES = {2337--2343}, }
Endnote
%0 Book Section %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Web Archives : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-53C1-B %R 10.1007/978-1-4614-6170-8_128 %D 2014 %B Encyclopedia of Social Network Analysis and Mining %P 2337 - 2343 %I Springer %C Berlin %@ 978-1-4614-6169-2
[264]
J. Biega, I. Mele, and G. Weikum, “Probabilistic Prediction of Privacy Risks in User Search Histories,” in PSBD’14, First International Workshop on Privacy and Security of Big Data, Shanghai, China, 2014.
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@inproceedings{Biega:PSBD2014, TITLE = {Probabilistic Prediction of Privacy Risks in User Search Histories}, AUTHOR = {Biega, Joanna and Mele, Ida and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-4503-1583-8}, DOI = {10.1145/2663715.2669609}, PUBLISHER = {ACM}, YEAR = {2014}, DATE = {2014}, BOOKTITLE = {PSBD'14, First International Workshop on Privacy and Security of Big Data}, PAGES = {29--36}, ADDRESS = {Shanghai, China}, }
Endnote
%0 Conference Proceedings %A Biega, Joanna %A Mele, Ida %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Probabilistic Prediction of Privacy Risks in User Search Histories : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-5486-B %R 10.1145/2663715.2669609 %D 2014 %B First International Workshop on Privacy and Security of Big Data %Z date of event: 2014-11-07 - 2014-11-07 %C Shanghai, China %K privacy risk prediction, probabilistic privacy, query logs, user-centric privacy %B PSBD'14 %P 29 - 36 %I ACM %@ 978-1-4503-1583-8
[265]
R. Burghartz and K. Berberich, “MPI-INF at the NTCIR-11 Temporal Query Classification Task,” in Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies, Tokyo, Japan, 2014.
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@inproceedings{burghartz2014, TITLE = {{MPI}-{INF} at the {NTCIR}-11 Temporal Query Classification Task}, AUTHOR = {Burghartz, Robin and Berberich, Klaus}, LANGUAGE = {eng}, ISBN = {978-4-86049-065-2}, PUBLISHER = {National Institute of Informatics}, YEAR = {2014}, BOOKTITLE = {Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies}, EDITOR = {Kando, Noriko and Joho, Hideo and Kishida, Kazuaki}, PAGES = {443--450}, ADDRESS = {Tokyo, Japan}, }
Endnote
%0 Conference Proceedings %A Burghartz, Robin %A Berberich, Klaus %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T MPI-INF at the NTCIR-11 Temporal Query Classification Task : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-5418-1 %D 2014 %8 09.12.2014 %B 11th NTCIR Conference on Evaluation of Information Access Technologies %Z date of event: 2014-12-09 - 2014-12-12 %C Tokyo, Japan %B Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies %E Kando, Noriko; Joho, Hideo; Kishida, Kazuaki %P 443 - 450 %I National Institute of Informatics %@ 978-4-86049-065-2 %U http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings11/pdf/NTCIR/Temporalia/03-NTCIR11-TEMPORALIA-BurghartzR.pdf
[266]
L. Del Corro, R. Gemulla, and G. Weikum, “Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning,” in The 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), Doha, Qatar, 2014.
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@inproceedings{DelCorro2014, TITLE = {Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning}, AUTHOR = {Del Corro, Luciano and Gemulla, Rainer and Weikum, Gerhard}, LANGUAGE = {eng}, ISBN = {978-1-937284-96-1}, URL = {http://aclweb.org/anthology/D14-1042}, PUBLISHER = {ACL}, YEAR = {2014}, BOOKTITLE = {The 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014)}, PAGES = {374--385}, ADDRESS = {Doha, Qatar}, }
Endnote
%0 Conference Proceedings %A Del Corro, Luciano %A Gemulla, Rainer %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-51DF-E %U http://aclweb.org/anthology/D14-1042 %D 2014 %B 2014 Conference on Empirical Methods in Natural Language Processing %Z date of event: 2014-10-25 - 2014-10-29 %C Doha, Qatar %B The 2014 Conference on Empirical Methods in Natural Language Processing %P 374 - 385 %I ACL %@ 978-1-937284-96-1
[267]
M. Dylla, M. Theobald, and I. Miliaraki, “Querying and Learning in Probabilistic Databases,” in Reasoning Web (RW 2014), Athens, Greece, 2014.
Abstract
Probabilistic Databases (PDBs) lie at the expressive intersection of databases, first-order logic, and probability theory. PDBs employ logical deduction rules to process Select-Project-Join (SPJ) queries, which form the basis for a variety of declarative query languages such as Datalog, Relational Algebra, and SQL. They employ logical consistency constraints to resolve data inconsistencies, and they represent query answers via logical lineage formulas (aka. "data provenance") to trace the dependencies between these answers and the input tuples that led to their derivation. While the literature on PDBs dates back to more than 25 years of research, only fairly recently the key role of lineage for establishing a closed and complete representation model of relational operations over this kind of probabilistic data was discovered. Although PDBs benefit from their efficient and scalable database infrastructures for data storage and indexing, they couple the data computation with probabilistic inference, the latter of which remains a #P-hard problem also in the context of PDBs. In this chapter, we provide a review on the key concepts of PDBs with a particular focus on our own recent research results related to this field. We highlight a number of ongoing research challenges related to PDBs, and we keep referring to an information extraction (IE) scenario as a running application to manage uncertain and temporal facts obtained from IE techniques directly inside a PDB setting.
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@inproceedings{DyllaRW2014, TITLE = {Querying and Learning in Probabilistic Databases}, AUTHOR = {Dylla, Maximilian and Theobald, Martin and Miliaraki, Iris}, LANGUAGE = {eng}, ISBN = {978-3-319-10587-1; 978-3-319-10586-4}, DOI = {10.1007/978-3-319-10587-1_8}, PUBLISHER = {Springer}, YEAR = {2014}, DATE = {2014}, ABSTRACT = {Probabilistic Databases (PDBs) lie at the expressive intersection of databases, first-order logic, and probability theory. PDBs employ logical deduction rules to process Select-Project-Join (SPJ) queries, which form the basis for a variety of declarative query languages such as Datalog, Relational Algebra, and SQL. They employ logical consistency constraints to resolve data inconsistencies, and they represent query answers via logical lineage formulas (aka. "data provenance") to trace the dependencies between these answers and the input tuples that led to their derivation. While the literature on PDBs dates back to more than 25 years of research, only fairly recently the key role of lineage for establishing a closed and complete representation model of relational operations over this kind of probabilistic data was discovered. Although PDBs benefit from their efficient and scalable database infrastructures for data storage and indexing, they couple the data computation with probabilistic inference, the latter of which remains a #P-hard problem also in the context of PDBs. In this chapter, we provide a review on the key concepts of PDBs with a particular focus on our own recent research results related to this field. We highlight a number of ongoing research challenges related to PDBs, and we keep referring to an information extraction (IE) scenario as a running application to manage uncertain and temporal facts obtained from IE techniques directly inside a PDB setting.}, BOOKTITLE = {Reasoning Web (RW 2014)}, EDITOR = {Koubarakis, Manolis and Stamou, Giorgos and Stoilos, Giorgos and Horrocks, Ian and Kolaitis, Phokion and Lausen, Georg and Weikum, Gerhard}, PAGES = {313--368}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {8714}, ADDRESS = {Athens, Greece}, }
Endnote
%0 Conference Proceedings %A Dylla, Maximilian %A Theobald, Martin %A Miliaraki, Iris %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Querying and Learning in Probabilistic Databases : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-E51D-9 %F OTHER: WOS:000348929200008 %R 10.1007/978-3-319-10587-1_8 %D 2014 %B 10th Reasoning Web Summer School %Z date of event: 2014-09-08 - 2014-09-13 %C Athens, Greece %X Probabilistic Databases (PDBs) lie at the expressive intersection of databases, first-order logic, and probability theory. PDBs employ logical deduction rules to process Select-Project-Join (SPJ) queries, which form the basis for a variety of declarative query languages such as Datalog, Relational Algebra, and SQL. They employ logical consistency constraints to resolve data inconsistencies, and they represent query answers via logical lineage formulas (aka. "data provenance") to trace the dependencies between these answers and the input tuples that led to their derivation. While the literature on PDBs dates back to more than 25 years of research, only fairly recently the key role of lineage for establishing a closed and complete representation model of relational operations over this kind of probabilistic data was discovered. Although PDBs benefit from their efficient and scalable database infrastructures for data storage and indexing, they couple the data computation with probabil