The LODeXporter: Flexible Generation of Linked Open Data Triples from NLP Frameworks for Automatic Knowledge Base Construction
Submitted by bahar on Thu, 2018-06-28 09:25
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Printer-friendly versionPDF version| Title | The LODeXporter: Flexible Generation of Linked Open Data Triples from NLP Frameworks for Automatic Knowledge Base Construction |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Refereed Designation | Refereed |
| Authors | Witte, R., and B. Sateli |
| Editors | Calzolari, 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 Name | Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) |
| Pagination | 2423-2428 |
| Date Published | May 7-12, 2018 |
| Publisher | European Language Resources Association (ELRA) |
| Conference Location | Miyazaki, Japan |
| ISBN Number | 979-10-95546-00-9 |
| Keywords | Automatic 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. |
| URL | https://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). |
| Attachment | Size |
|---|---|
| lodexporter-lrec2018.pdf | 234.03 KB |
