Generating and Executing Complex Natural Language Queries across Linked Data

Thierry Hamon; Fleur Mougin; Natalia Grabar

Generating and Executing Complex Natural Language Queries across Linked Data

Stud Health Technol Inform. 2015:216:815-20.

Authors

Thierry Hamon¹, Fleur Mougin², Natalia Grabar³

Affiliations

¹ LIMSI-CNRS, Orsay, France; Université Paris 13, Sorbonne Paris Cité, France.
² Université Bordeaux, ISPED, Centre INSERM U897, ERIAS, France.
³ STL UMR8163 CNRS, Université Lille 3, France.

PMID: 26262165

Abstract

With the recent and intensive research in the biomedical area, the knowledge accumulated is disseminated through various knowledge bases. Links between these knowledge bases are needed in order to use them jointly. Linked Data, SPARQL language, and interfaces in Natural Language question-answering provide interesting solutions for querying such knowledge bases. We propose a method for translating natural language questions in SPARQL queries. We use Natural Language Processing tools, semantic resources, and the RDF triples description. The method is designed on 50 questions over 3 biomedical knowledge bases, and evaluated on 27 questions. It achieves 0.78 F-measure on the test set. The method for translating natural language questions into SPARQL queries is implemented as Perl module available at http://search.cpan.org/ thhamon/RDF-NLP-SPARQLQuery.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Databases, Factual
Humans
Information Storage and Retrieval* / methods
Natural Language Processing*
Semantics