https://w3id.org/np/RA_KvkcBK2jEkoQuo82LEGQ0t6eYoZe8RTN1kIa_N2fW8
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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>,
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a <http://purl.org/spar/fabio/Workflow>;
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rdfs:label "Graph2Text Strategy" .
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rdfs:label "GraphRAG" .
<https://neverblink.eu/ontologies/llm-kg/methods#knowledgeGraphProgrammingLanguageRepresentationForLlmReasoning>
a <http://purl.org/spar/fabio/Workflow>;
dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#SynergizedKnowledgeRepresentation>;
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";
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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)" .
}
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