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Cross-lingual knowledge graph entity alignment based on relation awareness and attribute involvement

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Abstract

Entity alignment is an effective means of matching entities from various knowledge graphs (KGs) that represent the equivalent real-world object. With the development of representation learning, recent entity alignment methods learn entity structure representation by embedding KGs into a low-dimensional vector space, and then entity alignment relies on the distance between entity vectors. In addition to the graph structures, relations and attributes are also critical to entity alignment. However, most existing approaches ignore the helpful features included in relations and attributes. Therefore, this paper presents a new solution RAEA (Relation Awareness and Attribute Involvement for Entity Alignment), which includes relation and attribute features. Relation representation is incorporated into entity representation by Dual-Primal Graph CNN (DPGCNN), which alternates convolution-like operations on the original graph and its dual graph. Structure representation and attribute representation are learned by graph convolutional networks (GCNs). To further enrich the entity embedding, we integrate the textual information of the entity into the entity graph embedding. Moreover, we fine-tune the entity similarity matrix by integrating fine-grained features. Experimental results on three benchmark datasets from real-world KGs show that our approach has superior performance to other representative entity alignment approaches in most cases.

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The data used or analyzed during the current study are available from the corresponding author after the paper is accepted for publication.

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Acknowledgements

Thanks to all the authors for their hard work. This work is supported by the National Natural Science Foundation of China under grant No.61872163 and 61806084, Jilin Province Key Scientific and Technological Research and Development Project under grant No.20210201131GX, and Jilin Provincial Education Department project under grant No.JJKH20190160KJ.

Funding

This work is supported by the National Natural Science Foundation of China under grant No.61872163 and 61806084, Jilin Province Key Scientific and Technological Research and Development Project under grant No.20210201131GX, and Jilin Provincial Education Department project under grant No.JJKH20190160KJ.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Qianjin Street, Changchun, 130012, Jilin, China

    Beibei Zhu, Tie Bao, Junyi Wang & Tao Peng

  2. College of Software, Jilin University, Qianjin Street, Changchun, 130012, Jilin, China

    Lu Liu

  3. Key Laboratory of Symbol Computation and Knowledge Engineering for Ministry of Education, Jilin University, Qianjin Street, Changchun, 130012, Jilin, China

    Lu Liu & Tao Peng

  4. Department of Linguistics, University of Washington, WA98195-3770, Seattle, 98195, Washington, USA

    Jiayu Han

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  1. Beibei Zhu
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Contributions

Beibei Zhu: conduct experiments, write the first draft of the article and revise the manuscript; Tie Bao: formulation of overarching research goals, oversight for the research activity planning and execution; Jiayu Han: analyze and synthesize data; Lu Liu: provide funding for publication of the article and revise the article; Junyi Wang: visualization of data and production of graphs for the article; Tao Peng: revise the article, provide financial support and validate the experimental results.

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Correspondence to Tao Peng.

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Zhu, B., Bao, T., Liu, L. et al. Cross-lingual knowledge graph entity alignment based on relation awareness and attribute involvement. Appl Intell 53, 6159–6177 (2023). https://doi.org/10.1007/s10489-022-03797-6

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