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

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