KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph

• URL: http://arxiv.org/abs/2103.08893v1
• Date: Tue, 16 Mar 2021 07:32:33 GMT
• Title: KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph
• Authors: Yiying Yang, Xi Yin, Haiqin Yang, Xingjian Fei, Hao Peng, Kaijie Zhou, Kunfeng Lai, and Jianping Shen
• Abstract summary: We propose a novel entity synonyms discovery framework, named emphKGSynNet. Specifically, we pre-train subword embeddings for mentions and entities using a large-scale domain-specific corpus. We employ a specifically designed emphfusion gate to adaptively absorb the entities' knowledge information into their semantic features.
• Score: 23.053995137917994
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 (OOV) 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 \emph{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 \emph{fusion gate} to adaptively absorb the entities' knowledge information into their semantic features. We conduct extensive experiments to demonstrate the effectiveness of our \emph{KGSynNet} in leveraging the knowledge graph. The experimental results show that the \emph{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.

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