CHE-TKG: Collaborative Historical Evidence and Evolutionary Dynamics Learning for Temporal Knowledge Graph Reasoning

TL;DR

CHE-TKG introduces a dual-view learning framework for temporal knowledge graph reasoning, explicitly modeling historical evidence and evolutionary dynamics to improve future event prediction.

Contribution

It is the first to jointly model and leverage both historical evidence and evolutionary dynamics in TKG reasoning through a collaborative learning approach.

Findings

Achieves state-of-the-art results on multiple TKG benchmarks.

Effectively captures long-term structural regularities and recent temporal changes.

Demonstrates the benefit of dual-view modeling over single-view methods.

Abstract

Temporal knowledge graph (TKG) reasoning aims to predict future events from historical facts. A key challenge lies in jointly capturing two sources of predictive information in TKGs: historical evidence and evolutionary dynamics. However, existing methods typically focus on only one of these sources, which limits the ability to fully exploit the complementary predictive signals in TKGs. To address this, we propose CHE-TKG, a novel collaborative dual-view learning framework for TKG reasoning. CHE-TKG explicitly separates and jointly models historical evidence and evolutionary dynamics, aiming to learn and exploit their complementary predictive signals. Specifically, CHE-TKG constructs a historical evidence graph to capture long-term structural regularities and stable relational constraints, alongside an evolutionary dynamics graph to model temporal transitions and recent changes, with dedicated encoders for each view. We further employ relation decomposition and a contrastive alignment objective to better capture the predictive signals across the two views. Extensive experiments demonstrate that CHE-TKG achieves state-of-the-art performance on multiple benchmarks.