Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2021

Abstract

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.

Keywords

Entity alignment, Entity name, Graph structure, Iterative training

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'21), Virtual Conference, 2021 July 11-15

First Page

777

Last Page

786

ISBN

9781450380379

Identifier

10.1145/3404835.3462870

Publisher

ACM

City or Country

Virtual Event

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