https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE/Head https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE/assertion https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE/provenance https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE/pubinfo https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAf5eWtPoiy5hw6GzYR3WJ0pEXxxGYhyxBNg5g6Y-SDOE/assertion https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/dc/terms/title Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/spar/cito/describes https://neverblink.eu/ontologies/llm-kg/methods#GatedPromptChainingWorkflowForKgDmlConstruction https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/spar/cito/describes https://neverblink.eu/ontologies/llm-kg/methods#LlmAgentForInteractiveKgDiagnostics https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#DynamicMasterLogicModel https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#FdLlm https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#KgBasedInContextLearningForIndustrialSensorNetworks https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#KgDrivenAnalysisSystemForVehicleFaultDiagnostics https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#KgEmbeddedLlmArchitectureForCncMachineFaultDiagnosis https://doi.org/10.48550/arXiv.2505.21291 http://purl.org/spar/cito/discusses https://neverblink.eu/ontologies/llm-kg/methods#RootKgd https://doi.org/10.48550/arXiv.2505.21291 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#DynamicMasterLogicModel http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#DynamicMasterLogicModel http://www.w3.org/2000/01/rdf-schema#label Dynamic Master Logic (DML) model https://neverblink.eu/ontologies/llm-kg/methods#FdLlm http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#FdLlm http://www.w3.org/2000/01/rdf-schema#label FD-LLM https://neverblink.eu/ontologies/llm-kg/methods#GatedPromptChainingWorkflowForKgDmlConstruction http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#LLMAugmentedKGConstruction https://neverblink.eu/ontologies/llm-kg/methods#GatedPromptChainingWorkflowForKgDmlConstruction http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#GatedPromptChainingWorkflowForKgDmlConstruction http://www.w3.org/2000/01/rdf-schema#comment This method utilizes an LLM-based workflow, comprising sequential LLM calls for summarization, entity recognition, JSON structuring, and Cypher generation, to automatically construct a Knowledge Graph (KG-DML) from unstructured system documentation. The workflow integrates LLM-based validation gates and feedback loops to ensure accuracy and consistency in the KG construction process. The LLM's primary role is to enhance the KG construction task by automating the extraction and structuring of domain-specific knowledge. https://neverblink.eu/ontologies/llm-kg/methods#GatedPromptChainingWorkflowForKgDmlConstruction http://www.w3.org/2000/01/rdf-schema#label Gated Prompt Chaining Workflow for KG-DML Construction https://neverblink.eu/ontologies/llm-kg/methods#GatedPromptChainingWorkflowForKgDmlConstruction https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#LLMAugmentedKG https://neverblink.eu/ontologies/llm-kg/methods#KgBasedInContextLearningForIndustrialSensorNetworks http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#KgBasedInContextLearningForIndustrialSensorNetworks http://www.w3.org/2000/01/rdf-schema#label KG-based in-context learning for industrial sensor networks https://neverblink.eu/ontologies/llm-kg/methods#KgDrivenAnalysisSystemForVehicleFaultDiagnostics http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#KgDrivenAnalysisSystemForVehicleFaultDiagnostics http://www.w3.org/2000/01/rdf-schema#label KG-driven analysis system for vehicle fault diagnostics https://neverblink.eu/ontologies/llm-kg/methods#KgEmbeddedLlmArchitectureForCncMachineFaultDiagnosis http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#KgEmbeddedLlmArchitectureForCncMachineFaultDiagnosis http://www.w3.org/2000/01/rdf-schema#label KG-embedded LLM architecture for CNC machine fault diagnosis https://neverblink.eu/ontologies/llm-kg/methods#LlmAgentForInteractiveKgDiagnostics http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#SynergizedReasoning https://neverblink.eu/ontologies/llm-kg/methods#LlmAgentForInteractiveKgDiagnostics http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#LlmAgentForInteractiveKgDiagnostics http://www.w3.org/2000/01/rdf-schema#comment This method proposes an LLM agent that interprets natural language queries, acting as an orchestrator to select and execute external, structured KG reasoning tools (e.g., upward/downward propagation) for diagnostic tasks. For general interpretive queries, it employs a Graph-RAG approach by retrieving relevant KG segments and embedding them into the LLM's prompt. 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