Training-Free Hybrid Evidence Retrieval for Question Answering: Dynamic Fusion of Knowledge-Graph Triples and Dense Text Embeddings - Xavier, Otávio Calaça; Soares, Anderson da Silva (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2025: Anais do XL Simpósio Brasileiro de Bancos de Dados, 2025)
This work presents a sub-50ms, training-free retrieval pipeline that leverages a Neo4j knowledge graph and a ChromaDB vector index. Questions and passages are embedded with Sentence-BERT, and the retrieved entities seed a one-hop Cypher expansion in the knowledge graph. A transparent fusion based on Dice-Sørensen overlap ranks both passages and triples. On the WebQSP and CQA-12k benchmarks, this hybrid method achieves superior Recall@10, MRR, and nDCG@10 compared to BM25, graph-only, and vectoronly baselines. Requiring no learned parameters and running on commodity hardware, it offers a practical alternative to heavyweight neural re-rankers and a robust evidence layer for retrieval-augmented generation (RAG).