QuerioDALI: Question Answering Over Dynamic and Linked Knowledge Graphs

We present a domain-agnostic system for Question Answering over multiple semi-structured and possibly linked datasets without the need of a training corpus. The system is motivated by an industry use-case where Enterprise Data needs to be combined with a large body of Open Data to fulfill information needs not satisfied by prescribed application data models. Our proposed Question Answering pipeline combines existing components with novel methods to perform, in turn, linguistic analysis of a query, named entity extraction, entity/graph search, fusion and ranking of possible answers. We evaluate QuerioDALI with two open-domain benchmarks and a biomedical one over Linked Open Data sources, and show that our system produces comparable results to systems that require training data and are domain-dependent. In addition, we analyze the current challenges and shortcomings.

[1]  Michael Schroeder,et al.  Answering Factoid Questions in the Biomedical Domain , 2013, BioASQ@CLEF.

[2]  Jens Lehmann,et al.  Template-based question answering over RDF data , 2012, WWW.

[3]  Vanessa López,et al.  Data Access Linking and Integration with DALI: Building a Safety Net for an Ocean of City Data , 2015, SEMWEB.

[4]  Petr Baudis,et al.  Modeling of the Question Answering Task in the YodaQA System , 2015, CLEF.

[5]  Enrico Motta,et al.  Merging and Ranking Answers in the Semantic Web: The Wisdom of Crowds , 2009, ASWC.

[6]  Saeedeh Shekarpour,et al.  Semantic Interpretation of User Query for Question Answering on Interlinked Data , 2015 .

[7]  Elena Cabrio,et al.  Question Answering over Linked Data (QALD-5) , 2014, CLEF.

[8]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[9]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[10]  Peer Bork,et al.  The SIDER database of drugs and side effects , 2015, Nucleic Acids Res..

[11]  Gerhard Weikum,et al.  Robust question answering over the web of linked data , 2013, CIKM.

[12]  Dongyan Zhao,et al.  Answering Natural Language Questions via Phrasal Semantic Parsing , 2014, CLEF.

[13]  Hannah Bast,et al.  More Accurate Question Answering on Freebase , 2015, CIKM.

[14]  Christian Bizer,et al.  DBpedia spotlight: shedding light on the web of documents , 2011, I-Semantics '11.

[15]  Alexander Yates,et al.  Large-scale Semantic Parsing via Schema Matching and Lexicon Extension , 2013, ACL.

[16]  Sören Auer,et al.  SINA: Semantic interpretation of user queries for question answering on interlinked data , 2015, J. Web Semant..

[17]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[18]  Jens Lehmann,et al.  DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia , 2015, Semantic Web.

[19]  Siddharth Patwardhan,et al.  Structured data and inference in DeepQA , 2012, IBM J. Res. Dev..

[20]  Kalina Bontcheva,et al.  Improving habitability of natural language interfaces for querying ontologies with feedback and clarification dialogues , 2013, J. Web Semant..