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An inductive knowledge graph embedding via combination of subgraph and type information
Conventional knowledge graph representation learn the representation of entities and relations by projecting triples in the knowledge graph to a continuous vector space. The vector representation increases the precision of link prediction and the efficiency of downstream tasks. However, these method...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692217/ https://www.ncbi.nlm.nih.gov/pubmed/38040858 http://dx.doi.org/10.1038/s41598-023-48616-1 |
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author | Liu, Hongbo Chen, Yue He, Peng Zhang, Chao Wu, Hao Zhang, Jiange |
author_facet | Liu, Hongbo Chen, Yue He, Peng Zhang, Chao Wu, Hao Zhang, Jiange |
author_sort | Liu, Hongbo |
collection | PubMed |
description | Conventional knowledge graph representation learn the representation of entities and relations by projecting triples in the knowledge graph to a continuous vector space. The vector representation increases the precision of link prediction and the efficiency of downstream tasks. However, these methods cannot process previously unseen entities during the knowledge graph evolution. In other words, the model trained on the source knowledge graph cannot be applied to the target knowledge graph containing new unseen entities. Recently, a few subgraph-based link prediction models obtained the inductive ability, but they all neglect semantic information. In this work, we propose an inductive representation learning model TGraiL which considers not only the topological structure but also semantic information. First, distance in the subgraph is used to encode the node’s topological structure. Second, the projection matrix is used to encode the entity type information. Finally, both kinds of information are fused for training to acquire the ultimate vector representation of entities. The experimental results indicate that the model’s performance has been significantly improved compared to the existing baseline models, demonstrating the method’s effectiveness and superiority. |
format | Online Article Text |
id | pubmed-10692217 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106922172023-12-03 An inductive knowledge graph embedding via combination of subgraph and type information Liu, Hongbo Chen, Yue He, Peng Zhang, Chao Wu, Hao Zhang, Jiange Sci Rep Article Conventional knowledge graph representation learn the representation of entities and relations by projecting triples in the knowledge graph to a continuous vector space. The vector representation increases the precision of link prediction and the efficiency of downstream tasks. However, these methods cannot process previously unseen entities during the knowledge graph evolution. In other words, the model trained on the source knowledge graph cannot be applied to the target knowledge graph containing new unseen entities. Recently, a few subgraph-based link prediction models obtained the inductive ability, but they all neglect semantic information. In this work, we propose an inductive representation learning model TGraiL which considers not only the topological structure but also semantic information. First, distance in the subgraph is used to encode the node’s topological structure. Second, the projection matrix is used to encode the entity type information. Finally, both kinds of information are fused for training to acquire the ultimate vector representation of entities. The experimental results indicate that the model’s performance has been significantly improved compared to the existing baseline models, demonstrating the method’s effectiveness and superiority. Nature Publishing Group UK 2023-12-01 /pmc/articles/PMC10692217/ /pubmed/38040858 http://dx.doi.org/10.1038/s41598-023-48616-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liu, Hongbo Chen, Yue He, Peng Zhang, Chao Wu, Hao Zhang, Jiange An inductive knowledge graph embedding via combination of subgraph and type information |
title | An inductive knowledge graph embedding via combination of subgraph and type information |
title_full | An inductive knowledge graph embedding via combination of subgraph and type information |
title_fullStr | An inductive knowledge graph embedding via combination of subgraph and type information |
title_full_unstemmed | An inductive knowledge graph embedding via combination of subgraph and type information |
title_short | An inductive knowledge graph embedding via combination of subgraph and type information |
title_sort | inductive knowledge graph embedding via combination of subgraph and type information |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692217/ https://www.ncbi.nlm.nih.gov/pubmed/38040858 http://dx.doi.org/10.1038/s41598-023-48616-1 |
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