https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc/Head https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc/assertion https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc/provenance https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc/pubinfo https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAjG98Gpzlzwiy3i6yP7Fj1wTHVecNCfQVG0TPWBBT9uc/assertion https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/dc/terms/title Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/describes https://neverblink.eu/ontologies/llm-kg/methods#CLAKGLLMRecommendationFramework https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/describes https://neverblink.eu/ontologies/llm-kg/methods#LLMbasedCLAKGConstruction https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#BERT https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#DPCNN https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#GRU https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#GraphRAG https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#LightRAG https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#RGCN https://doi.org/10.48550/arXiv.2410.04949 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#TFIDFRAG https://doi.org/10.48550/arXiv.2410.04949 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/ns/prov#Entity https://neverblink.eu/ontologies/llm-kg/methods#BERT http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#BERT http://www.w3.org/2000/01/rdf-schema#label BERT https://neverblink.eu/ontologies/llm-kg/methods#CLAKGLLMRecommendationFramework http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#KGEnhancedLLMInference https://neverblink.eu/ontologies/llm-kg/methods#CLAKGLLMRecommendationFramework http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#CLAKGLLMRecommendationFramework http://www.w3.org/2000/01/rdf-schema#comment This closed-loop framework leverages the Case-Enhanced Law Article Knowledge Graph (CLAKG) to ground the recommendations generated by an LLM during the inference stage. By retrieving relevant historical case information and candidate law articles from the CLAKG, the framework enables the LLM to provide more accurate law article recommendations and effectively mitigates issues like hallucinations in LLM outputs, thereby enhancing LLM performance. https://neverblink.eu/ontologies/llm-kg/methods#CLAKGLLMRecommendationFramework http://www.w3.org/2000/01/rdf-schema#label CLAKG-LLM Law Article Recommendation Framework https://neverblink.eu/ontologies/llm-kg/methods#CLAKGLLMRecommendationFramework https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#KGEnhancedLLM https://neverblink.eu/ontologies/llm-kg/methods#DPCNN http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#DPCNN http://www.w3.org/2000/01/rdf-schema#label DPCNN https://neverblink.eu/ontologies/llm-kg/methods#GRU http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#GRU http://www.w3.org/2000/01/rdf-schema#label GRU https://neverblink.eu/ontologies/llm-kg/methods#GraphRAG http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#GraphRAG http://www.w3.org/2000/01/rdf-schema#label Graph-RAG https://neverblink.eu/ontologies/llm-kg/methods#LLMbasedCLAKGConstruction http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGConstruction https://neverblink.eu/ontologies/llm-kg/methods#LLMbasedCLAKGConstruction http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#LLMbasedCLAKGConstruction http://www.w3.org/2000/01/rdf-schema#comment This method uses a Large Language Model (LLM) to automatically extract nodes and relationships from law articles and judgments, integrating them to form the Case-Enhanced Law Article Knowledge Graph (CLAKG). 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