# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 3 Method
### Entity Extraction and Relationship Analysis
Mission Context: Wildfire Rescue; Captured Entities: fire source locations, fire spread direction, burning vegetation extent, smoke coverage areas \\ \cline{1-1}  & After the rainstorm disaster in Renhe District, Sichuan, drones captured [topographic changes in landslide areas] and [vegetation damage], generating high-precision 3D models to assist in post-disaster reconstruction planning. \\ \cline{1-1}  & Region Context: Post-Rainstorm Disaster Reconstruction; Captured Entities: terrain changes in landslide areas, vegetation destruction \\ \cline{1-1}  & Chongqing used drone aerial photography to record the current status of [urban roads], [buildings], and [rivers], providing basic geographic information for old city renovation and greening projects, enhancing planning efficiency. (Urban Planning Inspection\(-\)Roods, Buildings, Rivers) \\ \cline{1-1}  & Mission Context: Urban Planning Monitoring; Captured Entities: roads, buildings, rivers \\ \hline \multirow{8}{*}{Output} & During California’s wildfires, drones utilized infrared sensors to capture real-time data on [fire source locations] and [directions of fire spread], monitoring [forest burn areas] and [smoke-covered regions], assisting fire departments in formulating firefighting strategies. (California Wildfires\(-\)fire sources, fire spread, forest burn areas, smoke coverage) \\ \cline{1-1}  & After the rainstorm disaster in Renhe District, Sichuan, drones captured [topographic changes in landslide areas] and [vegetation damage], generating high-precision 3D models to assist in post-disaster reconstruction planning. (Renhe District Rainstorm Disaster \(-\)landslide areas, vegetation damage) \\ \cline{1-1}  & Chongqing used drone aerial photography to record the current status of [urban roads], [buildings], and [rivers], providing basic geographic information for old city renovation and greening projects, enhancing planning efficiency. (Chongqing Old City Renovation \(-\)urban roads, buildings, rivers) \\ \hline \multirow{8}{*}{iFLTTEK} & During California’s wildfires, drones utilized infrared sensors to capture real-time data on fire source locations and directions of fire spread, monitoring forest burn areas and smoke-covered regions, assisting fire departments in formulating firefighting strategies. After the rainstorm disaster in Renhe District, Sichuan, drones captured [topographic changes in landslide areas and vegetation damage], generating high-precision 3D models to assist in post-disaster reconstruction planning. \\ \cline{1-1}  & Chongqing used drone aerial photography to record the current status of urban roads, buildings, and rivers, providing basic geographic information for old city renovation and greening projects, enhancing planning efficiency. \\ \cline{1-1}  & Region Context: Urban Planning Monitoring; Captured Entities: roads, buildings, rivers \\ \cline{1-1}  & Region Context: Post-Rainstorm Disaster Reconstruction; Captured Entities: terrain changes in landslide areas, vegetation destruction \\ \cline{1-1}  & Region Context: Post-task configuration. As demonstrated in Table 3, this methodological refinement substantially reduced erroneous extraction instances across all evaluated models, enabling precise identification of atomic entities (e.g., \"smoke\") from contextual descriptions (e.g., \"smoke coverage areas\"). However, the extraction accuracy and effectiveness of the three LLMs still exhibited significant variations, which are quantitatively analyzed in Section 4. To establish robust relevance assessment criteria, we developed an LLM-driven analytical framework for evaluating entity-task relationships through semantic correlation analysis. This methodology introduces structured prompt templates (Equation (1)) to standardize scoring rationality, incorporating two fundamental evaluation dimensions:  
* Frequency Significance: Assessed through the frequency of occurrence of entities in different corpora under the same task.