Abstract
The goal of knowledge graph representation learning is to map entities and relations into low-dimensional continuous vector Spaces in order to learn their semantic information representation. However, most existing models often struggle to model the basic features of knowledge graphs effectively, such as symmetric/antisymmetric, inverse, and combinatorial relational patterns. In addition, many models ignored the information about the neighborhood of entities in the triples in the graph. In order to solve these problems, this paper proposes a learning model of three-dimensional rotating knowledge graph representation based on graph context. The model first uses the quaternion mathematical framework to represent the entity as a set of vectors in three-dimensional space, and interprets the relationship as a three-dimensional rotation transformation between the entities. Then, by calculating the semantic similarity between entities and relations, the graph context information is fused into the vector representation. Experiments on public data sets FB15K-237 and WN18RR demonstrate the effectiveness of the proposed model. The experimental results show that the model can capture the relational pattern of knowledge graph better and make full use of the neighborhood information of entities in the graph.
This work was supported by Natural Science Foundation of Shandong Province (No. ZR2022LZH008) and The 20 Planned Projects in Jinan (Nos. 2021GXRC046, 202228120).
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Acknowledgments
This work was supported by Natural Science Foundation of Shandong Province (No. ZR2022LZH008) and The 20 Planned Projects in Jinan (Nos. 2021GXRC046, 202228120).
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Chen, X., Geng, Y., Liang, H. (2024). Three-Dimensional Rotation Knowledge Representation Learning Based on Graph Context. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1962. Springer, Singapore. https://doi.org/10.1007/978-981-99-8132-8_27
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DOI: https://doi.org/10.1007/978-981-99-8132-8_27
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