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@prefix dct: <http://purl.org/dc/terms/> .
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sub:assertion {
  <https://doi.org/10.48550/arXiv.2507.04127> dct:title "BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#ByokgRag>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#AgenticTraversal>,
      <https://neverblink.eu/ontologies/llm-kg/methods#GraphReranker>, <https://neverblink.eu/ontologies/llm-kg/methods#LlmPlusGraphQuery>,
      <https://neverblink.eu/ontologies/llm-kg/methods#TextBasedRetrieval>;
    a prov:Entity .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#AgenticTraversal> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Agentic Traversal" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#ByokgRag> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGQuestionAnswering>;
    a <http://purl.org/spar/fabio/Workflow>;
    rdfs:comment "BYOKG-RAG is a novel framework that enhances Knowledge Graph Question Answering (KGQA) by synergistically combining LLMs with specialized graph retrieval tools. LLMs generate critical graph artifacts (entities, paths, queries) that are processed by graph tools for linking and retrieval. The retrieved context then enables the LLM to iteratively refine its graph linking and retrieval before generating the final answer.";
    rdfs:label "BYOKG-RAG";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#LLMAugmentedKG> .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#GraphReranker> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Graph Reranker" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#LlmPlusGraphQuery> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "LLM+graph-query" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#TextBasedRetrieval> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Text-based Retrieval" .
}

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    prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2507.04127> .
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