MAG: A multilingual, knowledge-base agnostic and deterministic entity linking approach

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

Authors

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results showthat the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.

Original languageEnglish
Title of host publicationProceedings of the Knowledge Capture Conference, K-CAP 2017
Number of pages8
PublisherAssociation for Computing Machinery, Inc
Publication date04.12.2017
Pages1-8
Article number9
ISBN (print)978-1-4503-5553-7
DOIs
Publication statusPublished - 04.12.2017
Externally publishedYes
EventK-CAP 2017: 9th International Conference on Knowledge Capture - Hilton Garden Inn Austin Downtown/Convention Center, Austin, United States
Duration: 04.12.201706.12.2017
https://k-cap2017.org

Bibliographical note

This work has been supported by the H2020 project HOBBIT (GA no. 688227) as well as the EuroStars projects DIESEL (no. 01QE1512C) and QAMEL (no. 01QE1549C) and supported by the Brazilian National Council for Scientific and Technological Development (CNPq) (no. 206971/2014-1). This work has also been supported by the German Federal Ministry of Transport and Digital Infrastructure (BMVI) in the projects LIMBO (no. 19F2029I) and OPAL (no. 19F2028A) as well as by the German Federal Ministry of Education and Research (BMBF) within ’KMU-innovativ: Forschung für die zivile Sicherheit’ in particular ’Forschung für die zivile Sicherheit’ and the project SOLIDE (no. 13N14456).

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