Combining embedding-based and symbol-based methods for entity alignment
作者：
Highlights：
• We propose a two-stage framework for entity alignment from the perspective of combining the advantages of both symbol-based and embedding-based methods.
• A series of symbol-based methods are adopted to align the relation pairs in stage I.
• Symbol-based methods and a hybrid embedding model are combined to match the entity pairs in stage II.
• Experimental results from real-word datasets demonstrate that our proposed method is effective.
• Ablation studies illustrate our proposed strategies are versatile and can also be applied to other embedding models.
摘要
•We propose a two-stage framework for entity alignment from the perspective of combining the advantages of both symbol-based and embedding-based methods.•A series of symbol-based methods are adopted to align the relation pairs in stage I.•Symbol-based methods and a hybrid embedding model are combined to match the entity pairs in stage II.•Experimental results from real-word datasets demonstrate that our proposed method is effective.•Ablation studies illustrate our proposed strategies are versatile and can also be applied to other embedding models.
论文关键词：Entity alignment,Knowledge graph embedding,String Similarity
论文评审过程：Received 21 January 2021, Revised 24 August 2021, Accepted 15 November 2021, Available online 17 November 2021, Version of Record 28 February 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108433
