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Entity alignment based on informative neighbor sampling and multi-embedding graph matching

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Abstract

Entity alignment is an important and necessary step in the process of knowledge fusion, which aims to match entities with the same meaning in different knowledge graphs. In this paper, we propose a novel entity alignment method based on informative neighbors sampling and multi-embedding graph matching (Multi-EINS). The graphs are embedded by graph convolutional network and the informative neighbors sampling is used to extract the neighborhood region topological structure feature to enhance the entity embedding. Relation and attribution information are embedded to incorporate former entity embedding, resulting representation-level embedding. Semantic and the character information are considered from outcome-level by calculate the distances of entities. The distance matrixes of multi-embedding are fused and the graph matching algorithm is performed on the fused matrix to align entities from different knowledge graphs. Experimental results on real datasets show that our proposed model effectively solves the entity alignment problem and outperforms 14 previous methods by 1% to 3% at least.

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Data Availibility Statement

Data sharing not applicable to this article as no datasets were generated during the current study. The data analysed during this study are provided with accessible links in this published article.

Notes

  1. https://github.com/facebookresearch/MUSE

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Funding

This work was supported in part by the National Key Research and Development Project of Ministry of Science and Technology of China under Grant 2020AAA0109300, in part by ”Science and Technology Innovation Action Plan” of Shanghai Science and Technology Commission for social development project under Grant 21DZ1204900, in part by Shanghai Local Capacity Enhancement project (No. 21010501500).

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  1. Shanghai University of Engineering Science, Longteng Road, 333, Shanghai, 201620, China

    Chunmei Liu, Yongbin Gao & Zhijun Fang

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  1. Chunmei Liu
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Correspondence to Yongbin Gao.

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Liu, C., Gao, Y. & Fang, Z. Entity alignment based on informative neighbor sampling and multi-embedding graph matching. Multimed Tools Appl 83, 34269–34289 (2024). https://doi.org/10.1007/s11042-023-16670-6

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