Embedding Method for Knowledge Graph with Densely Defined Ontology
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Abstract (arXiv)
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.
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References · 5
- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
- 2016Complex Embeddings for Simple Link Predictioncited by 897
- 2015Holographic Embeddings of Knowledge Graphscited by 181
- 2017A survey of embedding models of entities and relationships for knowledge graph completioncited by 8
- 2019Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI Systemcited by 2
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