1 research outputs found
Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
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 approx 8% improvements over the
baseline approach, and significantly outperform an end to end approach for
Wikidata entity linking.Comment: 15 page