Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning

In three linesQCEA reformulates medical entity alignment as a query-conditioned correspondence problem, integrating semantic encoding and graph-based representation learning. Evaluated on TCM-WM knowledge graphs (SymMap), the model improves Hit@K and MRR metrics, and demonstrates gains in RAG for evidence retrieval and answer accuracy.

Summary generated by Claude — human-verified