Query Answer Reformulation over Knowledge Bases

The answer of a query, submitted to a database or a knowledge base, is often long and may contain redundant data. The user is frequently forced to browse through a long answer or refine and repeat the query until the answer reaches a manageable size. Without proper treatment, consuming the answer may indeed become a tedious task. This article then proposes a process that modifies the presentation of a query answer to improve the quality of the user’s experience in the context of an RDF knowledge base. The process reorganizes the original query answer by applying heuristics to summarize the results and to select template questions that create a user dialog that guides the presentation of the results. The article also includes experiments based on RDF versions of MusicBrainz, enriched with DBpedia data, and IMDb, each with over 200 million RDF triples. The experiments use sample queries from well-known benchmarks.

[1]  Qinghua Zheng,et al.  A Survey of Faceted Search , 2013, J. Web Eng..

[2]  Bonnie L. Webber,et al.  Questions, Answers and Responses: Interacting with Knowledge-Base Systems , 1986, On Knowledge Base Management Systems.

[3]  M. Casanova,et al.  A Novel Solution for the Aggregation Problem in Natural Language Interface to Databases (NLIDB) , 2020, SBBD.

[4]  Thomas Pellissier Tanon,et al.  Question Answering Benchmarks for Wikidata , 2017, SEMWEB.

[5]  Jens Lehmann,et al.  Benchmarking Faceted Browsing Capabilities of Triplestores , 2017, SEMANTICS.

[6]  E. Prud hommeaux,et al.  SPARQL query language for RDF , 2011 .

[7]  Alfred C. Weaver,et al.  A framework for evaluating database keyword search strategies , 2010, CIKM.

[8]  Michael Zock,et al.  Trends in Natural Language Generation An Artificial Intelligence Perspective , 1996, Lecture Notes in Computer Science.

[9]  Mohand Boughanem,et al.  Novel Node Importance Measures to Improve Keyword Search over RDF Graphs , 2019, DEXA.

[10]  Eduard H. Hovy,et al.  Aggregation in Natural Language Generation , 1993, EWNLG.

[11]  Steffen Staab,et al.  TripleRank: Ranking Semantic Web Data by Tensor Decomposition , 2009, SEMWEB.

[12]  Aidan Hogan,et al.  GraFa: Faceted Search & Browsing for the Wikidata Knowledge Graph , 2018, International Semantic Web Conference.

[13]  Vanessa López,et al.  Core techniques of question answering systems over knowledge bases: a survey , 2017, Knowledge and Information Systems.