Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion

• URL: http://arxiv.org/abs/2408.06603v1
• Date: Tue, 13 Aug 2024 03:36:30 GMT
• Title: Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
• Authors: Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, Renhong Cheng,
• Abstract summary: 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. We propose TCompoundE, which is specially designed with two geometric operations, including time-specific and relation-specific operations.
• Score: 18.606006541284422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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