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@prefix sub: <https://w3id.org/np/RAHgq5zlls4_opZYqrqp3xTn5jXnlkcec_2iWHIY3MkA4/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix npx: <http://purl.org/nanopub/x/> .

sub:Head {
  this: np:hasAssertion sub:assertion;
    np:hasProvenance sub:provenance;
    np:hasPublicationInfo sub:pubinfo;
    a np:Nanopublication .
}

sub:assertion {
  <https://doi.org/10.48550/arXiv.2410.20724> dct:title "Published as a conference paper at ICLR 2025 SIMPLE IS EFFECTIVE: THE ROLES OF GRAPHS AND LARGE LANGUAGE MODELS IN KNOWLEDGE-GRAPH-BASED RETRIEVAL-AUGMENTED GENERATION";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#SubgraphRAG>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#CosineSimilarityRetriever>,
      <https://neverblink.eu/ontologies/llm-kg/methods#EtD>, <https://neverblink.eu/ontologies/llm-kg/methods#GNNRAG>,
      <https://neverblink.eu/ontologies/llm-kg/methods#GRetriever>, <https://neverblink.eu/ontologies/llm-kg/methods#KDCoT>,
      <https://neverblink.eu/ontologies/llm-kg/methods#RetrieveRewriteAnswer>, <https://neverblink.eu/ontologies/llm-kg/methods#RoG>,
      <https://neverblink.eu/ontologies/llm-kg/methods#SRPlusNSMwE2E>, <https://neverblink.eu/ontologies/llm-kg/methods#StructGPT>,
      <https://neverblink.eu/ontologies/llm-kg/methods#ToG>, <https://neverblink.eu/ontologies/llm-kg/methods#UniKGQA>;
    a prov:Entity .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#CosineSimilarityRetriever> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Cosine Similarity Retriever" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#EtD> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "EtD" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#GNNRAG> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "GNN-RAG" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#GRetriever> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "G-Retriever" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#KDCoT> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "KD-CoT" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#RetrieveRewriteAnswer> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Retrieve-Rewrite-Answer" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#RoG> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "RoG" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#SRPlusNSMwE2E> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "SR+NSM w E2E" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#StructGPT> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "StructGPT" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#SubgraphRAG> dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference>;
    a <http://purl.org/spar/fabio/Workflow>;
    rdfs:comment "SubgraphRAG is a KG-based RAG framework that retrieves relevant subgraphs from KGs using a lightweight MLP with Directional Distance Encoding (DDE) and parallel triple-scoring. It then employs unfine-tuned LLMs, guided by tailored prompts, to reason over the retrieved subgraphs, reducing hallucinations and improving answer grounding during the inference stage.";
    rdfs:label "SubgraphRAG";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#ToG> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "ToG" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#UniKGQA> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "UniKGQA" .
}

sub:provenance {
  sub:assertion prov:wasAttributedTo <https://neverblink.eu/ontologies/llm-kg/agent>;
    prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2410.20724> .
}

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