Make it easy: An effective end-to-end entity alignment framework

• Main Authors: GE, Congcong, LIU, Xiaoze, CHEN, Lu Chen, ZHENG, Baihua, GAO, Yunjun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Entity alignment; Entity name; Graph structure; Iterative training; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6127; https://ink.library.smu.edu.sg/context/sis_research/article/7130/viewcontent/3404835.3462870.pdf
• Institution: Singapore Management University

Entity alignment (EA) is a prerequisite for enlarging the coverage of a unified knowledge graph. Previous EA approaches either restrain the performance due to inadequate information utilization or need labor-intensive pre-processing to get external or reliable information to perform the EA task. This paper proposes EASY, an effective end-to-end EA framework, which is able to (i) remove the labor-intensive pre-processing by fully discovering the name information provided by the entities themselves; and (ii) jointly fuse the features captured by the names of entities and the structural information of the graph to improve the EA results. Specifically, EASY first introduces NEAP, a highly effective name-based entity alignment procedure, to obtain an initial alignment that has reasonable accuracy and meanwhile does not require much memory consumption or any complex training process. Then, EASY invokes SRS, a novel structure-based refinement strategy, to iteratively correct the misaligned entities generated by NEAP to further enhance the entity alignment. Extensive experiments demonstrate the superiority of our proposed EASY with significant improvement against 13 existing state-of-the-art competitors.