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Concordia University
Montréal, Canada

The LODeXporter: Flexible Generation of Linked Open Data Triples from NLP Frameworks for Automatic Knowledge Base Construction

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TitleThe LODeXporter: Flexible Generation of Linked Open Data Triples from NLP Frameworks for Automatic Knowledge Base Construction
Publication TypeConference Paper
Year of Publication2018
AuthorsWitte, R., and B. Sateli
Refereed DesignationRefereed
EditorsCalzolari, N., K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, and T. Tokunaga
Conference NameProceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
Pagination2423-2428
Date PublishedMay 7-12, 2018
PublisherEuropean Language Resources Association (ELRA)
Conference LocationMiyazaki, Japan
ISBN Number979-10-95546-00-9
KeywordsAutomatic Knowledge Base Construction, Linked Open Data, Semantic Web
Abstract

We present LODeXporter, a novel approach for exporting Natural Language Processing (NLP) results to a graph-based knowledge base, following Linked Open Data (LOD) principles. The rules for transforming NLP entities into Resource Description Framework (RDF) triples are described in a custom mapping language, which is defined in RDF Schema (RDFS) itself, providing a separation of concerns between NLP pipeline engineering and knowledge base engineering. LODeXporter is available as an open source component for the GATE (General Architecture for Text Engineering) framework.

URLhttps://www.aclweb.org/anthology/L18-1385
Acknowledgments

This work was partially funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (DG).

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