TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph

Multi-hop logical reasoning over knowledge graph plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding methods for reasoning focus on static KGs, while temporal knowledge graphs have not been fully explored. Reasoning over TKGs has two challenges: 1. The query should answer entities or timestamps; 2. The operators should consider both set logic on entity set and temporal logic on timestamp set. To bridge this gap, we introduce the multi-hop logical reasoning problem on TKGs and then propose the first temporal complex query embedding named Temporal Feature-Logic Embedding framework (TFLEX) to answer the temporal complex queries. Specifically, we utilize fuzzy logic to compute the logic part of the Temporal Feature-Logic embedding, thus naturally modeling all first-order logic operations on the entity set. In addition, we further extend fuzzy logic on timestamp set to cope with three extra temporal operators ( After, Before and Between ). Experiments on numerous query patterns demonstrate the effectiveness of our method.

Authors

• Xueyuan Lin School of Computer Science, Beijing University of Posts and Telecommunications
• Haihong E School of Computer Science, Beijing University of Posts and Telecommunications
• Chengjin Xu IDEA Research, International Digital Economy Academy
• Gengxian Zhou School of Computer Science, Beijing University of Posts and Telecommunications
• Haoran Luo School of Computer Science, Beijing University of Posts and Telecommunications, China
• Tianyi Hu National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, CASIA & School of Artificial Intelligence, UCAS
• Fenglong Su
• Ningyuan Li
• Mingzhi Sun

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