KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity Linking

Abstract

Entity linking is a fundamental task for a successful use of knowledge graphs in many information systems. It maps textual mentions to their corresponding entities in a given knowledge graph. However, with the rapid evolution of knowledge graphs, a large number of entities is continuously added over time. Performing entity linking on new, or unseen, entities poses a great challenge, as standard entity linking approaches require large amounts of labeled data for all new entities, and the underlying model must be regularly updated. To address this challenge, several zero-shot entity linking approaches have been proposed, which don't require additional labeled data to perform entity linking over unseen entities and new domains. Most of these approaches use large language models, such as BERT, to encode the textual description of the mentions and entities in a common embedding space, which allows linking mentions to unseen entities. While such approaches have shown good performance, one big drawback is that they are not able to exploit the entity symbolic information from the knowledge graph, such as entity types, relations, popularity scores and graph embeddings. In this paper, we present KG-ZESHEL, a knowledge graph-enhanced zero-shot entity linking approach, which extends an existing BERT-based zero-shot entity linking approach with mention and entity auxiliary information. Experiments on two benchmark entity linking datasets, show that our proposed approach outperforms the related BERT-based state-of-the-art entity linking models.