Enhancing Extrapolation Reasoning on Temporal Knowledge Graphs with Logic Rules and Queries

Abstract

Extrapolation reasoning on Temporal Knowledge Graphs (TKGs) plays a pivotal role in various systems, including retrieval, recommendation, and Q&A. Traditional TKG reasoning methods tend to emphasize modeling the local and global features of facts, often overlooking the alignment with query semantics. Crucially, these methods are challenging in generating explicit reasoning paths. To address these gaps, we propose an innovative framework (LogiQ) for extrapolation reasoning on TKGs, steered by temporal logic rules and queries. Specifically, LogiQ incorporates temporal logic rules and implements a rule-guided reward mechanism, directing Reinforcement Learning (RL) agents toward actions more aligned with rules. Additionally, LogiQ merges temporal queries with neighbor aggregation, ensuring that candidate actions not only encapsulate neighboring factual data but also embody query semantics. This dual focus enables LogiQ to guide actions to find reasoning paths in limited steps strategically. Extensive experiments on four real-world TKG datasets demonstrate the superior performance of LogiQ across all metrics compared to existing state-of-the-art models.