Hybrid retrieval that combines vector or semantic search with keyword matching or knowledge-graph structure appears independently in fire investigation (Choi & Cho 2026), smart manufacturing (Wan et al. 2025), and biomedicine (Matsumoto et al. 2024), suggesting a convergent design pattern for making domain RAG systems reliable.
E4 combines semantic and keyword-based searches in the fire-investigation pipeline, E14 combines knowledge-graph structure with vector retrieval in smart manufacturing, and E16's KRAGEN combines knowledge graphs with vector-database RAG in biomedicine — three independent instances of the hybrid retrieval pattern.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 1 Oct, 04:39
Source chain
AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“semantic and keyword-based searches are combined to improve accuracy and contextual understanding”
Abstract · no page number · evidence E4
- 02
“a hybrid KG-Vector RAG framework that systematically integrates structured KG metadata with unstructured vector retrieval is proposed”
Abstract · no page number · evidence E14
- 03
“KRAGEN converts knowledge graphs into a vector database and uses RAG to retrieve relevant facts from it”
Full-text passage · no page number · evidence E16
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.