AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Path-finding over knowledge graphs has become an effective way to ground LLM reasoning on multi-hop questions. However, biomedical QA introduces two distinct challenges that general-domain methods are not designed for: (i) queries do not expose intermediate reasoning and can be answered through multiple valid pathways, and (ii) biomedical knowledge graphs are densely connected, so path-finding methods easily take wrong turns. To address these challenges, we propose AdaPath, a path-finding framework that retrieves query-adaptive meta-paths from Path-Bank, which captures both query semantics and
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- PossiblePossibly related (embedding) · 47%Prompt-Based Knowledge Fusion Improves Faithfulness in Open-Domain Question Answering - Bioengineer.org →
- PossiblePossibly related (embedding) · 47%Empowering biomedical evidence exploration and synthesis with deep knowledge graph research →
- LinkedLinked via arxiv author · 85%Jun Hyeong Kim →
“AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA”
- LinkedLinked via arxiv author · 85%Dongki Kim →
“AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA”
- LinkedLinked via arxiv author · 85%Yinhua Piao →
“AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA”
- LinkedLinked via arxiv author · 85%Sung Ju Hwang →
“AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA”