🤖 AI Summary
This work addresses the limitation of existing multi-hop knowledge graph retrieval methods, which often neglect query semantics during traversal, resulting in low path relevance. The authors propose a query-aware diffusion activation approach that employs a semantic gating mechanism at each step, computed via cosine similarity between entity descriptions and the input question, to efficiently guide traversal within a fixed number of steps. Crucially, the entire retrieval process is encoded into a single Cypher query and executed in one step within Neo4j, eliminating the need for external memory loading. This method uniquely reduces query-aware traversal to fixed-iteration semantic gating entirely inside the graph database. Evaluated on MuSiQue, it achieves 32.80 EM, outperforming the HippoRAG baseline by 5.3 EM and 3.4 F1; ablation studies further demonstrate that semantic gating alone yields F1 improvements of 3.6–7.4 and reduces latency by 1.5–4.9×.
📝 Abstract
Retrieval-augmented generation built on knowledge graphs (Graph RAG) outperforms flat passage retrieval on multi-hop question answering by leveraging graph structure. In most existing systems, however, the question only sets the seed nodes; the subsequent traversal becomes "query-blind", depending solely on the graph structure. The exception is QAFD-RAG, which implements query-aware traversal via a flow-diffusion solver with combined edge re-weighting. This architecture requires loading the full graph into Python memory and an iterative solver with a variable number of iterations complicating integration with the graph database. We propose a spreading-activation method that achieves the same query-aware traversal with a single per-step semantic gate: the step weight is the cosine similarity between the candidate entity's description and the question, and the number of iterations is fixed. The whole retrieval procedure - seed mapping, propagation, top-K selection and context assembly - is expressed as a single Cypher query executed in one round-trip to Neo4j; the graph never leaves the database. On MuSiQue our method matches QAFD-RAG by exact match (32.80 vs 33.50) and outperforms the strongest purely-structural baseline in our comparison, HippoRAG, by 5.3 EM and 3.4 F1; on 2WikiMultiHopQA HippoRAG and QAFD-RAG retain an advantage due to their phrase-node architectures. An ablation with the gate disabled confirms that the gate is the source of a simultaneous F1 gain of 3.6 to 7.4 points and a retrieval-latency reduction by a factor of 1.5 to 4.9.