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

TL;DR

This paper proposes an attentive neural network model that leverages knowledge graph context to improve Wikidata entity linking, especially for long, implicit, or nonstandard entity titles, achieving significant performance gains.

Contribution

It introduces a novel approach that exploits knowledge graph context within an attentive neural network for more accurate entity linking on Wikidata.

Findings

Approximately 8% improvement over baseline methods

Significant outperforming of end-to-end approaches for Wikidata

Effective handling of long, implicit, and nonstandard entity titles

Abstract

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.