Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity

Jianhui Zhou, Yun Gao, Xiangbing Zhao, Yanping Guo, Xulong Zhang

Open source

DOI: 10.1371/journal.pone.0362390
Published: 2026-11-06
Container: PLOS One
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0362390,
  title = {Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity},
  author = {Jianhui Zhou and Yun Gao and Xiangbing Zhao and Yanping Guo and Xulong Zhang},
  year = {2026},
  journal = {PLOS One},
  doi = {10.1371/journal.pone.0362390},
  url = {https://doi.org/10.1371/journal.pone.0362390}
}

RIS

TY  - JOUR
TI  - Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity
AU  - Jianhui Zhou
AU  - Yun Gao
AU  - Xiangbing Zhao
AU  - Yanping Guo
AU  - Xulong Zhang
PY  - 2026
JO  - PLOS One
DO  - 10.1371/journal.pone.0362390
UR  - https://doi.org/10.1371/journal.pone.0362390
ER  -

APA

Zhou, J., Gao, Y., Zhao, X., Guo, Y., & Zhang, X. (2026). Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity. PLOS One. https://doi.org/10.1371/journal.pone.0362390

Source records

crossref · retrieved 2026-11-08T09:38:44.537Z