Abstract
Knowledge graph representation learning aims to embed kn-owledge facts into a continuous vector space, enabling models to capture semantic connections within and between triples. However, existing methods primarily focus on a single dimension of entities or relations, limiting their ability to learn knowledge facts. To address this issue, this paper proposes a dual-dimension refined representation model. At the entity level, we perform residual semantic stratification of entities based on modulus and phase information. At the relation level, we introduce an adaptive direction mapping property, allowing entities to have different mapping directions in different relations, and employ negative sampling to further enhance the model’s ability to refine relations. Experimental results show that our model exhibits outstanding link prediction performance on datasets such as WN18RR, FB15k-237, and UMLS. Through validation experiments, we substantiate our assumptions and analyses regarding datasets and model capabilities, thereby addressing the interpretability shortcomings of existing embedding models on underperforming datasets.
J. Cui and F. Pu—These authors contributed equally to this work.
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Acknowledgement
This research is supported by the Key R &D Program Project of Zhejiang Province (No. 2021C02004, 2019C01004), and Zhejiang Gongshang University “Digital+” Disciplinary Construction Management Project (No. SZJ2022A009).
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Cui, J., Pu, F., Yang, B. (2023). Dual-Dimensional Refinement of Knowledge Graph Embedding Representation. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_12
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