Querying biomedical Linked Data with natural language questions

Abstract : Recent and intensive research in the biomedical area enabled to accumulate and disseminate biomedical knowledge through various knowledge bases increasingly available on the Web. The exploitation of this knowledge requires to create links between these bases and to use them jointly. Linked Data, the SPARQL language and interfaces in natural language question answering provide interesting solutions for querying such knowledge bases. However, while using biomedical Linked Data is crucial, life-science researchers may have difficulties using the SPARQL language. Interfaces based on natural language question answering are recognized to be suitable for querying knowledge bases. In this paper, we propose a method for translating natural language questions into SPARQL queries. We use Natural Language Processing tools, semantic resources and RDF triple descriptions. We designed a four-step method which allows to linguistically and semantically annotate questions, to perform an abstraction of these questions, then to build a representation of the SPARQL queries, and finally to generate the queries. The method is designed on 50 questions over three biomedical knowledge bases used in the task 2 of the QALD-4 challenge framework and evaluated on 27 new questions. It achieves good performance with 0.78 F-measure on the test set. The method for translating questions into SPARQL queries is implemented as a Perl module and is available at http://search.cpan.org/~thhamon/RDF-NLP-SPARQLQuery/.
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Open Journal Of Semantic Web, Research Online Publishing (RonPub), 2016, 0, pp.1 - 19. 〈10.3233/SW-160244〉
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https://hal.archives-ouvertes.fr/hal-01426686
Contributeur : Natalia Grabar <>
Soumis le : mercredi 4 janvier 2017 - 18:04:34
Dernière modification le : mercredi 13 février 2019 - 01:26:34
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2015SWJ-QALD-POMELO.pdf
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Thierry Hamon, Natalia Grabar, Fleur Mougin. Querying biomedical Linked Data with natural language questions. Open Journal Of Semantic Web, Research Online Publishing (RonPub), 2016, 0, pp.1 - 19. 〈10.3233/SW-160244〉. 〈hal-01426686〉

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