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sub:assertion {
  <https://doi.org/10.48550/arXiv.2412.10654> dct:title "Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data";
    <http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#knowledgeGraphProgrammingLanguageRepresentationForLlmReasoning>;
    <http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#biranEtAlBackPatching>,
      <https://neverblink.eu/ontologies/llm-kg/methods#dernbachEtAlNeighborhoodEncoding>,
      <https://neverblink.eu/ontologies/llm-kg/methods#graph2TextStrategy>, <https://neverblink.eu/ontologies/llm-kg/methods#graphLLM>,
      <https://neverblink.eu/ontologies/llm-kg/methods#graphRAG>, <https://neverblink.eu/ontologies/llm-kg/methods#nieEtAlCodeStyleIclForKbqa>,
      <https://neverblink.eu/ontologies/llm-kg/methods#reasoningOnGraphRoG>;
    a prov:Entity .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#biranEtAlBackPatching> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Biran et al. Back-patching Approach" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#dernbachEtAlNeighborhoodEncoding>
    a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Dernbach et al. Neighborhood Partitioning and Encoding Scheme" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#graph2TextStrategy> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Graph2Text Strategy" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#graphLLM> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "GraphLLM" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#graphRAG> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "GraphRAG" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#knowledgeGraphProgrammingLanguageRepresentationForLlmReasoning>
    dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#SynergizedKnowledgeRepresentation>;
    a <http://purl.org/spar/fabio/Workflow>;
    rdfs:comment "This method proposes and evaluates representing Knowledge Graphs using programming language (Python) code, both statically and dynamically. This representation is then used to either prompt or fine-tune Large Language Models, aiming to deeply integrate KG structures and semantics into LLM representations. The goal is to enhance LLM multi-hop reasoning by enabling the LLM to effectively represent and process knowledge from both its natural language understanding and the structured KG input in a synergistic manner.";
    rdfs:label "Knowledge Graph Programming Language Representation for LLM Reasoning";
    <https://neverblink.eu/ontologies/llm-kg/hasTopCategory> <https://neverblink.eu/ontologies/llm-kg/top-categories#SynergizedLLMKG> .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#nieEtAlCodeStyleIclForKbqa> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Nie et al. Code-style In-Context Learning for KBQA" .
  
  <https://neverblink.eu/ontologies/llm-kg/methods#reasoningOnGraphRoG> a <http://purl.org/spar/fabio/Workflow>;
    rdfs:label "Reasoning on Graph (RoG)" .
}

sub:provenance {
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