{
  "abstract": "Entity alignment is a pivotal technology for integrating heterogeneous knowledge graphs, yet most embedding-based alignment methods destroy the inherent structural embedding information of knowledge graphs when unifying cross-graph embedding spaces, resulting in limited alignment accuracy and stability. To tackle this issue, this paper proposes a novel model for entity alignment tasks, which named as Graph Convolutional Network with Maximum Translated Similarity (GCN-MTS). GCN-MTS innovatively leverages a maximum translated similarity mechanism to optimize the adjacency matrix, degree matrix and loss function. This core design effectively maintains the original relative embedding structure of cross-graph entities during embedding space unification, which is the key improvement over existing approaches. Comprehensive experiments on 3 pairs of real-world knowledge graph dataset demonstrate the superior performance of GCN-MTS against advanced baseline methods. It achieves average performance gains of 5%–15% in Hits@1 and 4%–16% in Hits@10. Ablation results confirm that the optimized module brings a nearly 22% improvement in alignment metrics compared with the vanilla GCN. Furthermore, GCN-MTS presents excellent robustness and stability across different seed alignment quantities and seed combinations.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer and Network Engineering, Shanxi Datong University, Datong, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
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        0
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      "name": "Jianhui Zhou"
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    {
      "name": "Yun Gao"
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    {
      "name": "Xiangbing Zhao"
    },
    {
      "name": "Yanping Guo"
    },
    {
      "name": "Xulong Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2qzk13vjrt5hh.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0362390",
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  "id": "2qzk13vjrt5hh",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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  "openalex": "W7225412041",
  "pdf_url": null,
  "pmcid": null,
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  "published": "2026-11-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/2qzk13vjrt5hh.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:20:24Z",
      "native_id": "10.1371/journal.pone.0362390",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0362390"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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    {
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  "title": "Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity",
  "title_text": "Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:20:48Z",
  "url": "https://research.draconitic.com/paper/2qzk13vjrt5hh",
  "venue": "PLOS One",
  "versions": []
}