Temporal knowledge graphs forecasting based on explainable temporal relation tree-graph.

Abstract

In real-world temporal knowledge graphs, relationships among entities often exhibit complex temporal dynamics. Effectively modeling multi-hop temporal relation chains and enabling interpretable reasoning remain core challenges in temporal knowledge graph forecasting, which we address with our proposed model, TRTL (Temporal Relation Tree-based Learning). To tackle these challenges, we introduce a novel reasoning framework grounded in two complementary graph structures: the Sequence Grounding Graph, which captures temporal interval and entity's relation alignments; the Temporal Relation Tree Graph, which organizes multi-hop relation chains into interpretable and tree-structured reasoning paths. These structures are encoded using a Tree-LSTM enhanced with attention mechanisms, enabling the model to effectively capture temporal logic and long-range dependencies. The tree-based symbolical reasoning process provides interpretable evidence, enhancing the transparency and reliability of predictions. Experiments on two time-interval benchmarks demonstrate that TRTL significantly outperforms existing symbolic-based models.