Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
This work addresses the challenge of inferring missing facts in temporal knowledge graphs for applications like recommendation systems, though it appears incremental by building on existing embedding methods.
The paper tackles the problem of temporal knowledge graph completion by proposing TCompoundE, a method that uses two geometric operations to capture complex temporal dynamics, and it significantly outperforms existing models in experiments.
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.