Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning
The paper presents a novel approach called Query-Conditioned Entity Alignment (QCEA) for improving cross-system medical reasoning. This method addresses the limitations of traditional static entity alignment by incorporating query context and recognizing the asymmetry between systems. Experimental results indicate that QCEA significantly enhances alignment accuracy and retrieval effectiveness in integrative medical settings.
- ▪QCEA reformulates entity alignment as a query-conditioned correspondence problem.
- ▪The framework integrates semantic encoding and graph-based representation learning.
- ▪Experimental evaluations show consistent improvements over existing alignment methods.
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Computer Science > Artificial Intelligence arXiv:2605.18570 (cs) [Submitted on 18 May 2026] Title:Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning Authors:Yan Jiao, Jingran Xu, Pin-Han Ho, Limei Peng View a PDF of the paper titled Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning, by Yan Jiao and 3 other authors View PDF HTML (experimental) Abstract:Cross-domain knowledge alignment is essential for integrating heterogeneous medical systems, yet existing approaches typically treat entity alignment as a static matching problem, ignoring query context and cross-system asymmetry.
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