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OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models

A paper on using retrieval-augmented generation to improve biomedical code mapping leveraging ontology knowledge graphs and large language models.

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OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models

By Hui Feng, Yuntzu Yin, Emiliano Reynares, Jay NanavatiLecture notes in computer science
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The authors propose OntologyRAG, a method for better and faster biomedical code mapping. They leverage ontology knowledge graphs and large language models to improve the accuracy and efficiency of code mapping.

The approach uses retrieval-augmented generation to combine the strengths of both knowledge graphs and language models.

Abstract

The authors propose OntologyRAG, a method for better and faster biomedical code mapping. They leverage ontology knowledge graphs and large language models to improve the accuracy and efficiency of code mapping. The approach uses retrieval-augmented generation to combine the strengths of both knowledge graphs and language models.

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biomedical code mappingontology knowledge graphslarge language modelsretrieval-augmented generationKnowledge GraphsLarge Language ModelsRetrieval & RAGOntology & Taxonomy
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