Publications - Current Year

2025

Paper

A. Hogan, X. L. Dong, D. Vrandečić, and G. Weikum

“Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users’ Questions,” 2025. [Online]. Available: https://arxiv.org/abs/2501.06699.

Abstract

Much has been discussed about how Large Language Models, Knowledge Graphs and
Search Engines can be combined in a synergistic manner. A dimension largely
absent from current academic discourse is the user perspective. In particular,
there remain many open questions regarding how best to address the diverse
information needs of users, incorporating varying facets and levels of
difficulty. This paper introduces a taxonomy of user information needs, which
guides us to study the pros, cons and possible synergies of Large Language
Models, Knowledge Graphs and Search Engines. From this study, we derive a
roadmap for future research.

BibTeX

@online{Hogan_2501.06699,
TITLE = {Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions},
AUTHOR = {Hogan, Aidan and Dong, Xin Luna and Vrande{\v c}i{\'c}, Denny and Weikum, Gerhard},
LANGUAGE = {eng},
URL = {https://arxiv.org/abs/2501.06699},
EPRINT = {2501.06699},
EPRINTTYPE = {arXiv},
YEAR = {2025},
MARGINALMARK = {$\bullet$},
ABSTRACT = {Much has been discussed about how Large Language Models, Knowledge Graphs and<br>Search Engines can be combined in a synergistic manner. A dimension largely<br>absent from current academic discourse is the user perspective. In particular,<br>there remain many open questions regarding how best to address the diverse<br>information needs of users, incorporating varying facets and levels of<br>difficulty. This paper introduces a taxonomy of user information needs, which<br>guides us to study the pros, cons and possible synergies of Large Language<br>Models, Knowledge Graphs and Search Engines. From this study, we derive a<br>roadmap for future research.<br>},
}

Endnote

%0 Report
%A Hogan, Aidan
%A Dong, Xin Luna
%A Vrande&#269;i&#263;, Denny
%A Weikum, Gerhard
%+ External Organizations
External Organizations
External Organizations
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0010-7837-A
%U https://arxiv.org/abs/2501.06699
%D 2025
%X   Much has been discussed about how Large Language Models, Knowledge Graphs and<br>Search Engines can be combined in a synergistic manner. A dimension largely<br>absent from current academic discourse is the user perspective. In particular,<br>there remain many open questions regarding how best to address the diverse<br>information needs of users, incorporating varying facets and levels of<br>difficulty. This paper introduces a taxonomy of user information needs, which<br>guides us to study the pros, cons and possible synergies of Large Language<br>Models, Knowledge Graphs and Search Engines. From this study, we derive a<br>roadmap for future research.<br>
%K Computer Science, Artificial Intelligence, cs.AI,Computer Science, Information Retrieval, cs.IR,Computer Science, Symbolic Computation, cs.SC

Conference paper

M. Kaiser and G. Weikum

“Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering,” in The ACM Web Conference 2025 (WWW 2025), Sydney, Australia.

@inproceedings{Kaiser_WWW25,
TITLE = {Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering},
AUTHOR = {Kaiser, Magdalena and Weikum, Gerhard},
LANGUAGE = {eng},
DOI = {10.1145/3701716.3715544},
PUBLISHER = {ACM},
YEAR = {2025},
PUBLREMARK = {Accepted},
MARGINALMARK = {$\bullet$},
BOOKTITLE = {The ACM Web Conference 2025 (WWW 2025)},
ADDRESS = {Sydney, Australia},
}

Endnote

%0 Conference Proceedings
%A Kaiser, Magdalena
%A Weikum, Gerhard
%+ Databases and Information Systems, MPI for Informatics, Max Planck Society
Databases and Information Systems, MPI for Informatics, Max Planck Society
%T Preference-based Learning with Retrieval Augmented Generation for
  Conversational Question Answering : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0010-FBD2-6
%R 10.1145/3701716.3715544
%D 2025
%B ACM Web Conference
%Z date of event: 2025-04-28 - 2025-05-02
%C Sydney, Australia
%B The ACM Web Conference 2025
%I ACM

Conference paper

G. H. Torbati, A. Tigunova, G. Weikum, and A. Yates

“CUP: A Framework for Resource-Efficient Review-Based Recommenders,” in Advances in Information Retrieval (ECIR 2025), Lucca, Italy, 2025.

@inproceedings{Torbati_ECIR25,
TITLE = {{CUP}: {A} Framework for Resource-Efficient Review-Based Recommenders},
AUTHOR = {Torbati, Ghazaleh Haratinezhad and Tigunova, Anna and Weikum, Gerhard and Yates, Andrew},
LANGUAGE = {eng},
ISBN = {978-3-031-88710-9},
DOI = {10.1007/978-3-031-88711-6_23},
PUBLISHER = {Springer},
YEAR = {2025},
MARGINALMARK = {$\bullet$},
DATE = {2025},
BOOKTITLE = {Advances in Information Retrieval (ECIR 2025)},
EDITOR = {Hauff, Claudia and Macdonald, Craig and Jannach, Dietmar and Kazai, Gabriella and Nardini, Franco Maria and Pinelli, Fabio and Silvestri, Fabrizio and Tonellotto, Nicola},
PAGES = {360--375},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {15573},
ADDRESS = {Lucca, Italy},
}

Endnote

%0 Conference Proceedings
%A Torbati, Ghazaleh Haratinezhad
%A Tigunova, Anna
%A Weikum, Gerhard
%A Yates, Andrew
%+ 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 CUP: A Framework for Resource-Efficient Review-Based Recommenders : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0011-0BBF-B
%R 10.1007/978-3-031-88711-6_23
%D 2025
%B 47th European Conference on Information Retrieval
%Z date of event: 2025-04-06 - 2025-04-10
%C Lucca, Italy
%B Advances in Information Retrieval
%E Hauff, Claudia; Macdonald, Craig; Jannach, Dietmar; Kazai, Gabriella; Nardini, Franco Maria; Pinelli, Fabio; Silvestri, Fabrizio; Tonellotto, Nicola
%P 360 - 375
%I Springer
%@ 978-3-031-88710-9
%B Lecture Notes in Computer Science
%N 15573
%U https://rdcu.be/eh6FF

Conference paper

H. D. Tran, G. Weikum, and A. Yates

“Efficient and Effective Conversational Search with Tail Entity Selection,” in Advances in Information Retrieval (ECIR 2025), Lucca, Italy, 2025.

@inproceedings{Tran_ECIR25,
TITLE = {Efficient and Effective Conversational Search with Tail Entity Selection},
AUTHOR = {Tran, Hai Dang and Weikum, Gerhard and Yates, Andrew},
LANGUAGE = {eng},
ISBN = {978-3-031-88713-0},
DOI = {978-3-031-88714-7_26},
PUBLISHER = {Springer},
YEAR = {2025},
MARGINALMARK = {$\bullet$},
DATE = {2025},
BOOKTITLE = {Advances in Information Retrieval (ECIR 2025)},
EDITOR = {Hauff, Claudia and Macdonald, Craig and Jannach, Dietmar and Kazai, Gabriella and Nardini, Franco Maria and Pinelli, Fabio and Silvestri, Fabrizio and Tonellotto, Nicola},
PAGES = {275--283},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {15574},
ADDRESS = {Lucca, Italy},
}

Endnote

%0 Conference Proceedings
%A Tran, Hai Dang
%A Weikum, Gerhard
%A Yates, Andrew
%+ 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 Efficient and Effective Conversational Search with Tail Entity Selection : 
%G eng
%U http://hdl.handle.net/21.11116/0000-0011-0BC4-4
%R 978-3-031-88714-7_26
%D 2025
%B 47th European Conference on Information Retrieval
%Z date of event: 2025-04-06 - 2025-04-10
%C Lucca, Italy
%B Advances in Information Retrieval
%E Hauff, Claudia; Macdonald, Craig; Jannach, Dietmar; Kazai, Gabriella; Nardini, Franco Maria; Pinelli, Fabio; Silvestri, Fabrizio; Tonellotto, Nicola
%P 275 - 283
%I Springer
%@ 978-3-031-88713-0
%B Lecture Notes in Computer Science
%N 15574
%U https://rdcu.be/eh6Lu