Joint entity and relation linking using EARL

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or independent parallel tasks. In this demo paper, we present EARL, which performs entity linking and relation linking as a joint single task. The system determines the best semantic connection between all keywords of the question by referring to the knowledge graph. This is achieved by exploiting the connection density between entity candidates and relation candidates. EARL uses Bloom filters for faster retrieval of connection density and uses an extended label vocabulary for higher recall to improve the overall accuracy.

Original languageEnglish
Title of host publicationISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks : Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks,  co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 8th to 12th, 2018
EditorsMarieke van Erp, Medha Atre, Vanessa Lopez, Kavitha Srinivas, Carolina Fortuna
Number of pages4
Place of PublicationAachen
PublisherSun Site Central Europe (RWTH Aachen University)
Publication date2018
Publication statusPublished - 2018
Externally publishedYes
Event17th International Semantic Web Conference - ISWC 2018 - Asilomar Conference Grounds, Monterey, United States
Duration: 08.10.201812.10.2018
Conference number: 17
http://iswc2018.semanticweb.org/

Bibliographical note

Publisher Copyright:
© 2018 CEUR-WS. All rights reserved.

    Research areas

  • Entity Linking, Question Answering, Relation Linking
  • Informatics

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