tHR-Net: A Hybrid Reasoning Framework for Temporal Knowledge Graph

Entity prediction and relation prediction are the two major tasks of temporal knowledge graph (TKG) reasoning. The key to answering queries about future events is to understand historical trends and extract the information most likely to affect the future, i.e., the TKG reasoning task is both influenced by the trends of time-evolving graphs and directly driven by the facts relevant to a specific query. Existing methods mostly build models separately for these two characteristics, namely evolution representation learning and query-specific methods, failing to integrate these two crucial factors that determine reasoning results into a single framework. In this paper, we propose a novel temporal hybrid reasoning network (tHR-NET), simultaneously considering the modeling of graph feature space evolution and the enhancement of query-related feature representations in TKG. Specifically, we introduce a global graph space evolution module to extract graph trends, which influence entity/relation representations at each timestamp through a temporal view projection. Additionally, we propose a query-specific increment module for targeted enhancement of entity and relation representations, capturing query-related factors over extended durations. Through extensive experiments on real datasets, tHR-NET demonstrates distinct advantages in parallel entity and relation prediction.