https://w3id.org/np/RA7TQ8hLxkdou5TPCZkfivO8lkVszXK_5nyQIGG4npHDE
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dct:title "Supervised Fine-Tuning of Large Language Models for Domain-Specific Knowledge Graph Construction: A Case Study on Hunan's Historical Celebrities";
<http://purl.org/spar/cito/describes> <https://neverblink.eu/ontologies/llm-kg/methods#ZhaoEtAl2025HunanCelebrityKGConstruction>;
<http://purl.org/spar/cito/discusses> <https://neverblink.eu/ontologies/llm-kg/methods#ChainOfThought>,
<https://neverblink.eu/ontologies/llm-kg/methods#InstructUIEFramework>, <https://neverblink.eu/ontologies/llm-kg/methods#SelfInstructFramework> .
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<https://neverblink.eu/ontologies/llm-kg/methods#ZhaoEtAl2025HunanCelebrityKGConstruction>
a <http://purl.org/spar/fabio/Workflow>;
dct:subject <https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGConstruction>;
rdfs:comment "This method proposes a supervised fine-tuning approach for Large Language Models to enhance information extraction for domain-specific Knowledge Graph construction. It involves defining a fine-grained schema, constructing an instruction fine-tuning dataset, and employing LoRA for parameter-efficient fine-tuning of LLMs. The fine-tuned LLMs are then used to extract structured entities, relations, and properties from text to build a Knowledge Graph.";
rdfs:label "ZhaoEtAl2025HunanCelebrityKGConstruction";
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