Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916146168463360 |
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| author | Cai, Li Mao, Xin Wang, Zhihong Zhao, Shangqing Zhou, Yuhao Wu, Changxu Lan, Man |
| author_facet | Cai, Li Mao, Xin Wang, Zhihong Zhao, Shangqing Zhou, Yuhao Wu, Changxu Lan, Man |
| contents | Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time. Existing methods, operating in real or complex spaces, have demonstrated promising performance in this task. This paper advances beyond conventional approaches by introducing more expressive quaternion representations for TKGC within hypercomplex space. Unlike existing quaternion-based methods, our study focuses on capturing time-sensitive relations rather than time-aware entities. Specifically, we model time-sensitive relations through time-aware rotation and periodic time translation, effectively capturing complex temporal variability. Furthermore, we theoretically demonstrate our method's capability to model symmetric, asymmetric, inverse, compositional, and evolutionary relation patterns. Comprehensive experiments on public datasets validate that our proposed approach achieves state-of-the-art performance in the field of TKGC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02355 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space Cai, Li Mao, Xin Wang, Zhihong Zhao, Shangqing Zhou, Yuhao Wu, Changxu Lan, Man Machine Learning Artificial Intelligence Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time. Existing methods, operating in real or complex spaces, have demonstrated promising performance in this task. This paper advances beyond conventional approaches by introducing more expressive quaternion representations for TKGC within hypercomplex space. Unlike existing quaternion-based methods, our study focuses on capturing time-sensitive relations rather than time-aware entities. Specifically, we model time-sensitive relations through time-aware rotation and periodic time translation, effectively capturing complex temporal variability. Furthermore, we theoretically demonstrate our method's capability to model symmetric, asymmetric, inverse, compositional, and evolutionary relation patterns. Comprehensive experiments on public datasets validate that our proposed approach achieves state-of-the-art performance in the field of TKGC. |
| title | Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.02355 |