| 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. [11], and B. Sateli [12] |
| Editors | Calzolari, N. [13], K. Choukri [14], C. Cieri [15], T. Declerck [16], S. Goggi [17], K. Hasida [18], H. Isahara [19], B. Maegaard [20], J. Mariani [21], H. Mazo [22], A. Moreno [23], J. Odijk [24], S. Piperidis [25], and T. Tokunaga [26] |
| 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 [27], Linked Open Data [28], Semantic Web [29] |
| 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 [30] |
| 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 [31] | 234.03 KB |
