KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph

Entity synonyms discovery is crucial for entity-leveraging applications. However, existing studies suffer from several critical issues: (1) the input mentions may be out-of-vocabulary (00V) and may come from a different semantic space of the entities; (2) the connection between mentions and entities may be hidden and cannot be established by surface matching; and (3) some entities rarely appear due to the long-tail effect. To tackle these challenges, we facilitate knowledge graphs and propose a novel entity synonyms discovery framework, named KGSynNet. Specifically, we pre-train subword embeddings for mentions and entities using a large-scale domain-specific corpus while learning the knowledge embeddings of entities via a joint TransC-TransE model. More importantly, to obtain a comprehensive representation of entities, we employ a specifically designed fusion gate to adaptively absorb the entities' knowledge information into their semantic features. We conduct extensive experiments to demonstrate the effectiveness of our KGSynNet in leveraging the knowledge graph. The experimental results show that the KGSynNet improves the state-of-the-art methods by 14.7% in terms of hits@3 in the offline evaluation and outperforms the BERT model by 8.3% in the positive feedback rate of an online A/B test on the entity linking module of a question answering system.

Automated summary

Entity synonyms discovery is crucial for applications, and the KGSynNet framework effectively addresses the challenges through the utilization of knowledge graphs and specific design.