Enhancing Temporal Knowledge Graph for Future Event Prediction with Long-Term Dense Graph

Temporal knowledge graph (TKG) reasoning has emerged as a pivotal approach in event prediction. An important yet challenging task in TKG reasoning is to predict future events by extrapolating from historical events and their correlations. Existing methods either overlook the modeling of long-term dependencies between entities or are ineffective in aggregating long-term information with recent facts. Motivated by dual process theory in cognitive sciences, we introduce TKG-LDG, an approach enhancing TKG for future entity prediction with long-term dense graph, to model event evolution in an adaptive manner. We first construct a unified dense graph from historical data to capture long-term dependencies, reflecting cumulative knowledge of entity interactions over time. This unified dense graph is compatible with any graph neural network and facilitates entity interaction learning from a long-term perspective. Then we initialize a TKG encoder from the unified dense graph to enhance short-term event interaction modeling. TKG-LDG effectively marries global context with local adaptability to recent temporal changes through its short-term recurrent encoders, in a way that mirrors human reasoning by integrating both long-term and short-term event dynamics. Extensive experiments conducted on six widely used TKG datasets demonstrate that our model outperforms strong baselines in future event prediction.