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

Diego Moussallem; Ricardo Usbeck; Michael Röder; Axel Cyrille Ngonga Ngomo

doi:10.1145/3148011.3148024

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

Diego Moussallem^*
, Ricardo Usbeck
, Michael Röder
, Axel Cyrille Ngonga Ngomo

^*Corresponding author for this work

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

38 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings of the Knowledge Capture Conference, K-CAP 2017
Number of pages	8
Publisher	Association for Computing Machinery, Inc
Publication date	04.12.2017
Pages	1-8
Article number	9
ISBN (Print)	978-1-4503-5553-7
DOIs	https://doi.org/10.1145/3148011.3148024 https://doi.org/10.48550/arXiv.1707.05288
Publication status	Published - 04.12.2017
Externally published	Yes
Event	The 9th International Conference on Knowledge Capture - K-CAP 2017: 9th International Conference on Knowledge Capture - Austin, Texas, USA, Austin, United States Duration: 04.12.2017 → 06.12.2017 Conference number: 9 http://www.k-cap2017.org (Official Event Website) 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).

Publisher Copyright:
© 2017 Copyright held by the owner/author(s).

Research areas and keywords

Entity Linking
Multilingual
Named Entity Disambiguation
Informatics
Business informatics

Access to Document

10.1145/3148011.3148024
10.48550/arXiv.1707.05288Licence: CC0

Cite this

Moussallem, D., Usbeck, R., Röder, M., & Ngomo, A. C. N. (2017). MAG: A multilingual, knowledge-base agnostic and deterministic entity linking approach. In Proceedings of the Knowledge Capture Conference, K-CAP 2017 (pp. 1-8). Article 9 (Proceedings of the Knowledge Capture Conference, K-CAP 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3148011.3148024, https://doi.org/10.48550/arXiv.1707.05288

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abstract = "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.",

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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 {\textquoteright}KMU-innovativ: Forschung f{\"u}r die zivile Sicherheit{\textquoteright} in particular {\textquoteright}Forschung f{\"u}r die zivile Sicherheit{\textquoteright} and the project SOLIDE (no. 13N14456). Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; The 9th International Conference on Knowledge Capture - K-CAP 2017 : 9th International Conference on Knowledge Capture, K-CAP 2017 ; Conference date: 04-12-2017 Through 06-12-2017",

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Moussallem, D, Usbeck, R, Röder, M & Ngomo, ACN 2017, MAG: A multilingual, knowledge-base agnostic and deterministic entity linking approach. in Proceedings of the Knowledge Capture Conference, K-CAP 2017., 9, Proceedings of the Knowledge Capture Conference, K-CAP 2017, Association for Computing Machinery, Inc, pp. 1-8, The 9th International Conference on Knowledge Capture - K-CAP 2017, Austin, Texas, United States, 04.12.17. https://doi.org/10.1145/3148011.3148024, https://doi.org/10.48550/arXiv.1707.05288

MAG: A multilingual, knowledge-base agnostic and deterministic entity linking approach. / Moussallem, Diego; Usbeck, Ricardo; Röder, Michael et al.
Proceedings of the Knowledge Capture Conference, K-CAP 2017. Association for Computing Machinery, Inc, 2017. p. 1-8 9 (Proceedings of the Knowledge Capture Conference, K-CAP 2017).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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AU - Moussallem, Diego

AU - Usbeck, Ricardo

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N2 - 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.

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KW - Named Entity Disambiguation

KW - Informatics

KW - Business informatics

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Moussallem D, Usbeck R, Röder M, Ngomo ACN. MAG: A multilingual, knowledge-base agnostic and deterministic entity linking approach. In Proceedings of the Knowledge Capture Conference, K-CAP 2017. Association for Computing Machinery, Inc. 2017. p. 1-8. 9. (Proceedings of the Knowledge Capture Conference, K-CAP 2017). doi: 10.1145/3148011.3148024, 10.48550/arXiv.1707.05288

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

Abstract

Bibliographical note

Research areas and keywords

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Cite this