TY - CHAP

T1 - MAG

T2 - K-CAP 2017

AU - Moussallem, Diego

AU - Usbeck, Ricardo

AU - Röder, Michael

AU - Ngomo, Axel Cyrille Ngonga

N1 - 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).

PY - 2017/12/4

Y1 - 2017/12/4

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.

AB - 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.

KW - Entity Linking

KW - Multilingual

KW - Named Entity Disambiguation

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85040571618&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/e415dc14-00af-324d-b2cb-3106fc863441/

U2 - 10.1145/3148011.3148024

DO - 10.1145/3148011.3148024

M3 - Article in conference proceedings

AN - SCOPUS:85040571618

SN - 978-1-4503-5553-7

T3 - Proceedings of the Knowledge Capture Conference, K-CAP 2017

SP - 1

EP - 8

BT - Proceedings of the Knowledge Capture Conference, K-CAP 2017

PB - Association for Computing Machinery, Inc

Y2 - 4 December 2017 through 6 December 2017

ER -