AutoSchema framework enables AI agents to query diverse knowledge graphs without training

Researchers have developed AutoSchema, a novel framework designed to enable language model agents to query heterogeneous knowledge graphs without prior training. This system addresses the challenge of varying schemas, identifiers, and links across different life science knowledge resources. AutoSchema inspects live schemas, maps question entities to graph identifiers, and explores relation paths to construct queries iteratively. Evaluations on biomedical and chemistry knowledge graph tasks demonstrated improved accuracy and efficiency compared to existing methods like TogoMCP, with preliminary evidence suggesting its capability to support unseen graphs. AI

IMPACT This framework could streamline AI agent interaction with complex, disparate life science data, accelerating research and discovery.

RANK_REASON This is a research paper detailing a new framework for querying knowledge graphs. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• AutoSchema
• BioASQ Task B
• CatalyzeX Code Finder for Papers
• Chemistry Knowledge Graph Transfer
• DagsHub
• Gotit.pub
• Hugging Face
• Longitudinal Biomedical Semantic QA
• Multi Resource Biomedical KGQA
• Resource Description Framework
• Resource Focused Biomedical KGQA
• ScienceCast
• SPARQL
• TogoMCP

• paper
• infra

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