knowledge-graph-reasoning

This paper proposes the Structure-Internalized Rule Language Model (SIRLM) to address reasoning evidence perception drift in knowledge graph reasoning with LLMs, improving faithfulness and effectiveness by coupling structural and parametric knowledge, with experiments showing superiority over state-of-the-art methods.

This paper introduces GRiD, a framework that uses diffusion models and reinforcement learning to generate graph-like rules (e.g., cycles, branches) for knowledge graph reasoning, addressing the limitations of existing chain-rule mining methods. Experiments on six benchmarks show competitive performance in KG completion tasks.

This paper proposes a strikingness-aware evaluation framework for Temporal Knowledge Graph Reasoning (TKGR) that weights events by rarity to better assess model reasoning, addressing overestimation from trivial repeated events.