PATHSMOOTHING: Mitigating Semantic Drift and Hub Noise via Path Evidence in KG-based RAG

Knowledge graph-based retrieval-augmented generation (KG-based RAG) connects information across text passages to support answer generation. Recent studies have shown that the main limitation of KG-based RAG lies in retrieving excessive irrelevant information, researchers have proposed optimization methods centered on path filtering. However, existing methods focus on structural factors when conducting retrieval, while ignoring the semantic factors, which results in the introduction of noise nodes. To overcome this, we propose PathSmoothing that uses personalized PageRank to aggregate the structural evidence scores of entities. It enumerates context paths containing entities in the subgraph, calculates the semantic relevance of the paths, and then smooths these scores by combining the original semantic scores of these triples. PathSmoothing filters triples and retrieves their source text as evidence for answer generation. Experiments on six datasets show that PathSmoothing achieves an average win rate of 54.13% against PathRAG across four evaluation dimensions on domain-specific tasks, and increases the average answer matching score from 50.24 to 52.02 on multi-hop question answering datasets. PathSmoothing also improves LightRAG and PathRAG when applied as a plugin to them. Our code and datasets are available at supplementary material.