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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2504.02889 |
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| _version_ | 1866912307989184512 |
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| author | Ugai, Takanori |
| author_facet | Ugai, Takanori |
| contents | Knowledge graph embedding (KGE) is a technique that enhances knowledge graphs by addressing incompleteness and improving knowledge retrieval. A limitation of the existing KGE models is their underutilization of ontologies, specifically the relationships between properties. This study proposes a KGE model, TransU, designed for knowledge graphs with well-defined ontologies that incorporate relationships between properties. The model treats properties as a subset of entities, enabling a unified representation. We present experimental results using a standard dataset and a practical dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02889 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embedding Method for Knowledge Graph with Densely Defined Ontology Ugai, Takanori Social and Information Networks Artificial Intelligence Knowledge graph embedding (KGE) is a technique that enhances knowledge graphs by addressing incompleteness and improving knowledge retrieval. A limitation of the existing KGE models is their underutilization of ontologies, specifically the relationships between properties. This study proposes a KGE model, TransU, designed for knowledge graphs with well-defined ontologies that incorporate relationships between properties. The model treats properties as a subset of entities, enabling a unified representation. We present experimental results using a standard dataset and a practical dataset. |
| title | Embedding Method for Knowledge Graph with Densely Defined Ontology |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2504.02889 |