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Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking

: Mulang, I.O.; Singh, K.; Vyas, A.; Shekarpour, S.; Vidal, M.-E.; Auer, S.


Huang, Z.:
Web Information Systems Engineering - WISE 2020. 21st International Conference. Proceedings. Pt.I : Amsterdam, The Netherlands, October 20-24, 2020
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12342)
ISBN: 978-3-030-62004-2 (Print)
ISBN: 978-3-030-62005-9 (Online)
ISBN: 978-3-030-62006-6
International Conference on Web Information Systems Engineering (WISE) <21, 2020, Amsterdam>
Fraunhofer IAIS ()

The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows 8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking.