A Multi-angle Bidirectional Interaction Model for Entity Linking

Abstract

Abstract The process of connecting references in a text to appropriate entities in a knowledge graph is known as entity linking. The existing entity linking model learns local compatibility through content and global interdependencies through relevant knowledge graph for disambiguation. However, the local compatibility component of existing methods usually ignores the multi-angle interactions between mentions and candidate entities. In order to fully account for the bidirectional connection between the input document and the knowledge graph, we propose the Bidirectional Interaction Entity Linking (BI-INTEL). The correlation between mentions and candidate entities, as well as the applicability of mention context and candidate entity descriptions, are all taken into account in the local compatibility component. In the global interdependence component, our stacked random walk layers learn the global interdependence of the candidate entity to enhance the accuracy of entity linking. According to the experiments, our BI-INTEL performs 3% better on average than cutting-edge methods.