Knowledge bases have become valuable assets for search and analytics. However, they have become so large and heterogeneous that users struggle with formulating queries -- even when supported by form-based or faceted user interfaces. This calls for new modes of interactive search and exploration of knowledge bases and associated datasets. For example, a life scientist or political scientist may rapidly collect tens of interesting datasets for a specific study, but would then drastically lose her productivity when trying to join different data items and search for patterns, trends and insight.
We believe the most effective way of relieving the user from the necessity to cope with the complex structure of the data, is by means of natural language for question answering and other interactions. User inputs such as "Which love songs did Bob Dylan write?" can be translated into structured SPARQL queries. The key difficulty here is to understand the question structure and to bridge the gap between the user's input vocabulary and the terminology in the knowledge base, for example, mapping "write" to a composer predicate. Starting with our work on the DEANNA system, published in the WWW 2012 and EMNLP 2012 conferences, and recent works published in the WWW 2018, NAACL-HLT 2019, and SIGIR 2019 conferences, we have been pursuing this objective of translating user questions into structured queries.
Major challenges that we address in our ongoing work are complex questions and questions that cannot be answered by the underlying knowledge base alone. For example, the question "Which European singers covered Bob Dylan?" involves joining entities of different types across different relations like composer and performed. The corresponding SPARQL query would necessarily require multiple variables. The incompleteness of knowledge bases is an obstacle for questions such as "Which love songs did Bob Dylan write about his wife?", as the lyrics and themes of songs would be captured only in textual form in Web documents and online communities. This group is headed by Rishiraj Saha Roy.
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This project presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines.
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs, Xiaolu Lu, Soumajit Pramanik, Rishiraj Saha Roy, Abdalghani Abujabal, Yafang Wang, and Gerhard Weikum, SIGIR 2019.
To bridge the gap between capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real questions that capture the various phenomena of interest, and the associated diversity in formulation patterns. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as compositionality, temporal reasoning, and comparisons. ComQA questions are selected from the WikiAnswers community QA platform, which typically contains questions that are not satisfactorily answerable by current search engines. Through a large crowdsourcing effort, we (i) extract factoid questions from the platform and group them into paraphrase clusters (such interrogative paraphrases have been showed to be very useful in developing robustness to syntactic variations), and (ii) annotate these question clusters with their answers from Wikipedia. ComQA contains 11, 214 questions grouped into 4, 834 paraphrase clusters. We describe this construction process in detail, highlighting measures taken to ensure high quality of the output. We also present an extensive analysis of our dataset, including performances of state-of-the-art systems, that demonstrate how ComQA can effectively drive future research.
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters, Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum, NAACL-HLT 2019. [Data] [BibTeX]
Translating natural language questions to semantic representations such as SPARQL is a core challenge in open-domain question answering over knowledge bases (KB-QA). Existing methods rely on a clear separation between an offline training phase, where a model is learned, and an online phase where this model is deployed. Two major shortcomings of such methods are that (i) they require access to a large annotated training set that is not always readily available and (ii) they fail on questions from before-unseen domains. To overcome these limitations, this project presents NEQA, a continuous learning paradigm for KB-QA. Offline, NEQA automatically learns templates mapping syntactic structures to semantic ones from a small number of training question-answer pairs. Once deployed, continuous learning is triggered on cases where templates are insufficient. Using a semantic similarity function between questions and by judicious invocation of non-expert user feedback, NEQA learns new templates that capture previously-unseen syntactic structures. This way, NEQA gradually extends its template repository. NEQA periodically re-trains its underlying models, allowing it to adapt to the language used after deployment. Our experiments demonstrate NEQA’s viability, with steady improvement in answering quality over time, and the ability to answer questions from new domains.
Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases, Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum, WWW 2018. [Slides] [BibTeX]
Templates are an important asset for question answering over knowledge graphs, simplifying the semantic parsing of input utterances and generating structured queries for interpretable answers. Stateof-the-art methods rely on hand-crafted templates with limited coverage. This project presents QUINT, a system that automatically learns utterance-query templates solely from user questions paired with their answers. Additionally, QUINT is able to harness language compositionality for answering complex questions without having any templates for the entire question. Experiments with different benchmarks demonstrate the high quality of QUINT.
Automated Template Generation for Question Answering over Knowledge Graphs, Abdalghani Abujabal, Mohamed Yahya, Mirek Riedewald, and Gerhard Weikum, WWW 2017. [Slides] [Data]
QUINT: Interpretable Question Answering over Knowledge Bases, Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum, EMNLP 2017. [Demo] [Poster] [BibTeX]
Question answering over knowledge bases (KB-QA) poses challenges in handling complex questions that need to be decomposed into sub-questions. An important case, addressed in this project, is that of temporal questions, where cues for temporal relations need to be discovered and handled. We propose TEQUILA, an enabler method for temporal QA that can run on top of any KB-QA engine. TEQUILA has four stages. It detects if a question has temporal intent. It decomposes and rewrites the question into non-temporal sub-questions and temporal constraints. Answers to sub-questions are then retrieved from the underlying KB-QA engine. Finally, TEQUILA uses constraint reasoning on temporal intervals to compute final answers to the full question. Comparisons against state-of-the-art baselines show the viability of our method.
TempQuestions: A Benchmark for Temporal Question Answering, Zhen Jia, Abdalghani Abujabal, Rishiraj Saha Roy, Jannik Strötgen, and Gerhard Weikum, HQA 2018. [Slides] [Data] [BibTeX]
This project investigates the problem of answering compositional factoid questions over knowledge bases (KB) under efficiency constraints. The method, called TIPI, (i) decomposes compositional questions, (ii) predicts answer types for individual sub-questions, (iii) reasons over the compatibility of joint types, and finally, (iv) formulates compositional SPARQL queries respecting type constraints. TIPI's answer type predictor is trained using distant supervision, and exploits lexical, syntactic and embedding-based features to compute context- and hierarchy-aware candidate answer types for an input question. Experiments on a recent benchmark show that TIPI results in state-of-the-art performance under the real-world assumption that only a single SPARQL query can be executed over the KB, and substantial reduction in the number of queries in the more general case.
Efficiency-aware Answering of Compositional Questions using Answer Type Prediction, David Ziegler, Abdalghani Abujabal, Rishiraj Saha Roy, and Gerhard Weikum, IJCNLP 2017. [Poster] [BibTeX]
Entity search over text corpora is not geared for relationship queries where answers are tuples of related entities and where a query often requires joining cues from multiple documents. With large knowledge graphs, structured querying on their relational facts is an alternative, but often suffers from poor recall because of mismatches between user queries and the knowledge graph or because of weakly populated relations. This project presents the TriniT search engine for querying and ranking on extended knowledge graphs that combine relational facts with textual web contents. Our query language is designed on the paradigm of SPO triple patterns, but is more expressive, supporting textual phrases for each of the SPO arguments. We present a model for automatic query relaxation to compensate for mismatches between the data and a user’s query. Query answers - tuples of entities - are ranked by a statistical language model. We present experiments with different benchmarks, including complex relationship queries, over a combination of the YAGO knowledge graph and the entity-annotated ClueWeb09 corpus.
Relationship Queries on Extended Knowledge Graphs, Mohamed Yahya, Denilson Barbosa, Klaus Berberich, Qiuyue Wang, and Gerhard Weikum, WSDM 2016.
Knowledge bases and the Web of Linked Data have become important assets for search, recommendation, and analytics. Natural-language questions are a user-friendly mode of tapping this wealth of knowledge and data. However, question answering technology does not work robustly in this setting as questions have to be translated into structured queries and users have to be careful in phrasing their questions. This project advocates a new approach that allows questions to be partially translated into relaxed queries, covering the essential but not necessarily all aspects of the user's input. To compensate for the omissions, we exploit textual sources associated with entities and relational facts. Our system translates user questions into an extended form of structured SPARQL queries, with text predicates attached to triple patterns. Our solution is based on a novel optimization model, cast into an integer linear program, for joint decomposition and disambiguation of the user question. We demonstrate the quality of our methods through experiments with the QALD benchmark.
Robust Question Answering over the Web of Linked Data, Mohamed Yahya, Klaus Berberich, Shady Elbassuoni, and Gerhard Weikum, CIKM 2013.
Natural Language Questions for the Web of Data, Mohamed Yahya, Klaus Berberich, Shady Elbassuoni, Maya Ramanath, Volker Tresp, and Gerhard Weikum, EMNLP 2012.
- Zhen Jia, Southwest Jiaotong University, China
- Xiaolu Lu, RMIT University, Australia
- Mirek Riedewald, Northeastern University, USA
- Jannik Strötgen, Bosch Center for AI, Germany
- Yafang Wang, Ant Financial Services Group, Hangzhou, China