O-M-Flow: Ontology-Constrained Evidence Retrieval for RAG over Aviation Maintenance Documents

Aviation maintenance questions often require evidence scattered across several passages, including possible causes, exclusion statements, operating conditions, and manual references. This study presents O-M-Flow, an ontology-constrained evidence retrieval method for retrieval-augmented generation (RAG). A lightweight, task-oriented ontology defines the roles and attributes of maintenance evidence. Document content is organized into an Episode–Facet–FacetPoint–Entity graph, and these attributes guide candidate selection, ranking, and evidence packaging. The ontology is manually defined by the research team and populated through deterministic rules. Evaluation uses three documents containing 457 pages and 100 question records, corresponding to 77 distinct questions and 46 groups of related or repeated questions. Under a shared text-embedding configuration, O-M-Flow with its original scoring procedure achieved a groundedness score of 0.8939, compared with 0.7366 for the keyword-retrieval baseline. An additional version implementing the explicit weighted score achieved 0.8667. Its source-page recall was 0.9809, while key-role coverage was 0.5352. Weight sensitivity, repeated reranking, and computational-cost measurements further characterize the method. The results support role-aware evidence organization on this benchmark and show that the additional explicit score does not consistently improve retrieval. Incomplete role coverage and limited independent human evaluation remain important constraints on interpretation.