Graph-based Retrieval-Augmented Generation (GraphRAG) can fail in interdisciplinary medicine where evidence comes from incommensurable knowledge systems, e.g., Traditional Chinese Medicine (TCM) and Western Medicine (WM). Similarity-driven retrieval in a shared latent space may over-unify heterogeneous concepts, causing semantic collapse (spurious alignments, provenance-blind mixing, and broken multi-hop chains). We propose Xonto-RAG, a cross-ontology GraphRAG framework that avoids ontological unification by constructing a dual-ontology graph with typed bridges induced via LLM-assisted verify-and-vote. A query-aware Relational Graph Attention Network (RGAT) then assembles compact, provenance-tracked multi-hop evidence subgraphs for grounded generation. Across TCM–WM benchmarks, Xonto-RAG outperforms strong RAG/GraphRAG baselines, seeing up to +22.2% accuracy on MLEC-QA TCWM (vs. OG-RAG), 77.0% accuracy with a 7B backbone, and 85.1% faithfulness on MLEC-TCM, while reducing semantic-collapse failures and yielding interpretable reasoning chains.

Retrieval-augmented generation ranks passages by topical relevance or semantic similarity, but generation needs passages that actually support a target claim. We study when these targets come apart, framing the task as claim-conditioned support ranking over a constructed candidate shortlist: given a query, an explicit target claim, and a small fixed set of candidates, rank them by evidential support. We build a controlled, typed development set of 175 controlled contrastive examples spanning seven spurious-evidence cues, each pairing one supporting passage with one targeted non-support passage expressing the designated cue, plus neutral distractors. We evaluate with a corrected, tie-aware protocol that uses role-free evaluator-facing opaque identifiers and neutral tie handling. Tested query-only relevance rankers (lexical, dense, and a cross-encoder) yield tie-aware focal-pair error rates of roughly 18–34%; in the BM25 cue decomposition, several strata have high error, including negation and relation directionality; a gold-claim NLI reference—which receives information the rankers do not—reduces but does not eliminate the error. We argue that evidence-ranking evaluation should explicitly condition on the claim whose support is assessed, and sketch a typed, claim-conditioned evaluation direction that separates relevance, support, and source attributes. This is a controlled diagnostic on development data: it does not estimate real-world prevalence or provide a deployable method.