Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
<|reference_start|>Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion: Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. However, these methods only adopt a single operation, which may have limitations in capturing the complex temporal dynamics present in temporal knowledge graphs. Therefore, we propose a simple but effective method, i.e. TCompoundE, which is specially designed with two geometric operations, including time-specific and relation-specific operations. We provide mathematical proofs to demonstrate the ability of TCompoundE to encode various relation patterns. Experimental results show that our proposed model significantly outperforms existing temporal knowledge graph embedding models. Our code is available at https://github.com/nk-ruiying/TCompoundE.<|reference_end|>
@article{ying2024simple,
    title={Simple but Effective Compound Geometric Operations for Temporal
  Knowledge Graph Completion},
    author={Rui Ying and Mengting Hu and Jianfeng Wu and Yalan Xie and Xiaoyi Liu
  and Zhunheng Wang and Ming Jiang and Hang Gao and Linlin Zhang and Renhong
  Cheng},
    journal={arXiv preprint arXiv:2408.06603},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06603},
    primaryClass={cs.AI}
}