Deep answers for naturally asked questions on the web of data

  • Authors:

  • Mohamed Yahya;Klaus Berberich;Shady Elbassuoni;Maya Ramanath;Volker Tresp;Gerhard Weikum

  • Affiliations:

  • Max-Planck Institute for Informatics, Saarbrücken, Germany;Max-Planck Institute for Informatics, Saarbrücken, Germany;Max-Planck Institute for Informatics, Saarbrücken, Germany;Indian Institute of Technology, Delhi, New Delhi, India;Siemens Corporate Research and Technologies, Munich, Germany;Max-Planck Institute for Informatics, Saarbrücken, Germany

  • Venue:
  • Proceedings of the 21st international conference companion on World Wide Web
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present DEANNA, a framework for natural language question answering over structured knowledge bases. Given a natural language question, DEANNA translates questions into a structured SPARQL query that can be evaluated over knowledge bases such as Yago, Dbpedia, Freebase, or other Linked Data sources. DEANNA analyzes questions and maps verbal phrases to relations and noun phrases to either individual entities or semantic classes. Importantly, it judiciously generates variables for target entities or classes to express joins between multiple triple patterns. We leverage the semantic type system for entities and use constraints in jointly mapping the constituents of the question to relations, classes, and entities. We demonstrate the capabilities and interface of DEANNA, which allows advanced users to influence the translation process and to see how the different components interact to produce the final result.