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- arXiv.2505.21291 type Entity assertion.
- DynamicMasterLogicModel type Workflow assertion.
- FdLlm type Workflow assertion.
- GatedPromptChainingWorkflowForKgDmlConstruction type Workflow assertion.
- KgBasedInContextLearningForIndustrialSensorNetworks type Workflow assertion.
- KgDrivenAnalysisSystemForVehicleFaultDiagnostics type Workflow assertion.
- KgEmbeddedLlmArchitectureForCncMachineFaultDiagnosis type Workflow assertion.
- LlmAgentForInteractiveKgDiagnostics type Workflow assertion.
- RootKgd type Workflow assertion.
- DynamicMasterLogicModel label "Dynamic Master Logic (DML) model" assertion.
- FdLlm label "FD-LLM" assertion.
- GatedPromptChainingWorkflowForKgDmlConstruction label "Gated Prompt Chaining Workflow for KG-DML Construction" assertion.
- KgBasedInContextLearningForIndustrialSensorNetworks label "KG-based in-context learning for industrial sensor networks" assertion.
- KgDrivenAnalysisSystemForVehicleFaultDiagnostics label "KG-driven analysis system for vehicle fault diagnostics" assertion.
- KgEmbeddedLlmArchitectureForCncMachineFaultDiagnosis label "KG-embedded LLM architecture for CNC machine fault diagnosis" assertion.
- LlmAgentForInteractiveKgDiagnostics label "LLM Agent for Interactive KG Diagnostics" assertion.
- RootKgd label "Root-KGD" assertion.
- GatedPromptChainingWorkflowForKgDmlConstruction 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." assertion.
- LlmAgentForInteractiveKgDiagnostics 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. This represents a synergized reasoning approach where the LLM and KG mutually enhance diagnostic capabilities by combining LLM's natural language understanding and agentic behavior with the KG's structured knowledge and reasoning tools." assertion.
- arXiv.2505.21291 describes GatedPromptChainingWorkflowForKgDmlConstruction assertion.
- arXiv.2505.21291 describes LlmAgentForInteractiveKgDiagnostics assertion.
- arXiv.2505.21291 discusses DynamicMasterLogicModel assertion.
- arXiv.2505.21291 discusses FdLlm assertion.
- arXiv.2505.21291 discusses KgBasedInContextLearningForIndustrialSensorNetworks assertion.
- arXiv.2505.21291 discusses KgDrivenAnalysisSystemForVehicleFaultDiagnostics assertion.
- arXiv.2505.21291 discusses KgEmbeddedLlmArchitectureForCncMachineFaultDiagnosis assertion.
- arXiv.2505.21291 discusses RootKgd assertion.
- GatedPromptChainingWorkflowForKgDmlConstruction subject LLMAugmentedKGConstruction assertion.
- LlmAgentForInteractiveKgDiagnostics subject SynergizedReasoning assertion.
- arXiv.2505.21291 title "Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework" assertion.
- GatedPromptChainingWorkflowForKgDmlConstruction hasTopCategory LLMAugmentedKG assertion.
- LlmAgentForInteractiveKgDiagnostics hasTopCategory SynergizedLLMKG assertion.