PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning

Temporal Knowledge Graphs (TKGs) enable effective modeling of knowledge dynamics and event evolution, facilitating deeper insights and analysis into temporal information. Recently, extrapolation of TKG reasoning has attracted great significance due to its remarkable ability to capture historical correlations and predict future events. Existing studies of extrapolation aim mainly at encoding the structural and temporal semantics based on snapshot sequences, which contain graph aggregators for the association within snapshots and recurrent units for the evolution. However, these methods are limited to modeling long-distance history, as they primarily focus on capturing temporal correlations over shorter periods. Besides, a few approaches rely on compiling historical repetitive statistics of TKGs for predicting future facts. But they often overlook explicit interactions in the graph structure among concurrent events. To address these issues, we propose a P otentia L concurr E nt A ggregation and contra S tive learn ING (PLEASING) method for TKG extrapolation. PLEASING is a two-step reasoning framework that effectively leverages the historical and potential features of TKGs. It includes two encoders for historical and global events with an adaptive gated mechanism, acquiring predictions with appropriate weight of the two aspects. Specifically, PLEASING constructs two auxiliary graphs to capture temporal interaction among timestamps and correlations among potential concurrent events, respectively, enabling a holistic investigation of temporal characteristics and future potential possibilities in TKGs. Furthermore, PLEASING incorporates contrastive learning to strengthen its capacity to identify whether queries are related to history. Extensive experiments on seven benchmark datasets demonstrate the state-of-the-art performances of PLEASING and its comprehensive ability to model TKG semantics.