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This method proposes an end-to-end framework for constructing and evaluating clinical knowledge graphs from free-text using a multi-agent LLM pipeline and a schema-constrained Retrieval-Augmented Generation (KG-RAG) strategy. It primarily leverages LLMs for entity discovery, attribute extraction, relation identification, ontology mapping, semantic encoding, and multi-LLM consensus validation, all contributing to the construction and refinement of knowledge graphs.
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