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sub:assertion {
  <https://doi.org/10.48550/arXiv.2403.05881> a prov:Entity;
    dct:title "KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#KgRank>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#BaizeHealthcare>,
      <https://neverblink.eu/ontologies/llm-kg/methods#CohereRerankModel>, <https://neverblink.eu/ontologies/llm-kg/methods#Gpt4>,
      <https://neverblink.eu/ontologies/llm-kg/methods#LLaMa2>, <https://neverblink.eu/ontologies/llm-kg/methods#MedCpt>,
      <https://neverblink.eu/ontologies/llm-kg/methods#UmlsBert> .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#BaizeHealthcare> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "baize-healthcare" .
  
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    rdfs:label "Cohere re-rank model" .
  
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    rdfs:label "GPT-4" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#KgRank> a <http://purl.org/spar/fabio/Workflow>;
    dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference>;
    rdfs:comment "KG-Rank is an augmented LLM framework that integrates a medical Knowledge Graph with novel ranking and re-ranking techniques. It identifies entities, retrieves relevant KG triples, and applies methods like Similarity Ranking, Answer Expansion Ranking, and MMR Ranking, followed by MedCPT-based re-ranking to provide relevant and precise information to LLMs during inference. The primary goal is to improve the factual consistency and reliability of LLM-generated long-form answers in medical Question Answering.";
    rdfs:label "KG-Rank";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  
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  <https://neverblink.eu/ontologies/llm-kg/methods#UmlsBert> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "UmlsBERT" .
}

sub:provenance {
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