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sub:Head {
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sub:assertion {
  <https://doi.org/10.48550/arXiv.2504.03137> a prov:Entity;
    dct:title "LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#lightprof>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#agentBench>,
      <https://neverblink.eu/ontologies/llm-kg/methods#embedKgqa>, <https://neverblink.eu/ontologies/llm-kg/methods#graftNet>,
      <https://neverblink.eu/ontologies/llm-kg/methods#kgt5>, <https://neverblink.eu/ontologies/llm-kg/methods#knowledgeNavigator>,
      <https://neverblink.eu/ontologies/llm-kg/methods#kvmem>, <https://neverblink.eu/ontologies/llm-kg/methods#nsm>,
      <https://neverblink.eu/ontologies/llm-kg/methods#pullNet>, <https://neverblink.eu/ontologies/llm-kg/methods#structGpt>,
      <https://neverblink.eu/ontologies/llm-kg/methods#tog>, <https://neverblink.eu/ontologies/llm-kg/methods#transferNet>,
      <https://neverblink.eu/ontologies/llm-kg/methods#uniKgqa> .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#agentBench> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "AgentBench" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#embedKgqa> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "EmbedKGQA" .
  
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    rdfs:label "GraftNet" .
  
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    rdfs:label "KGT5" .
  
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    rdfs:label "KnowledgeNavigator" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#kvmem> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "KV-Mem" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#lightprof> a <http://purl.org/spar/fabio/Workflow>;
    dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference>;
    rdfs:comment "LightPROF is a Retrieve-Embed-Reason framework that uses a Knowledge Adapter to encode both textual and structural information from Knowledge Graphs into LLM-friendly soft prompts. These prompts are injected during the LLM's inference stage to enhance its reasoning capabilities on KGQA tasks, particularly for small-scale LLMs, without retraining the LLM itself. This approach directly improves LLM performance during inference through KG integration.";
    rdfs:label "LightPROF";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM> .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#nsm> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "NSM" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#pullNet> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "PullNet" .
  
  <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#tog> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "ToG" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#transferNet> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "TransferNet" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#uniKgqa> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "UniKGQA" .
}

sub:provenance {
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    prov:wasDerivedFrom <https://doi.org/10.48550/arXiv.2504.03137> .
}

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