# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 3 Method
### Entity Extraction and Relationship Analysis
Spring Festival Emergency Management in Guangzhou During the 2025 Spring Festival, Guangzhou’s Emergency Command Center utilized drones for real-time monitoring of the Baietan fireworks display. Using AI-powered cloud platform cameras and high-definition imaging, the drones analyzed crowd and vehicle flows, quantified densities, and promptly identified risks, such as overcrowding and traffic bottlenecks. Simultaneously, drones patrolled key forested areas, like Hulu Mountain and Baishuizhai, to monitor wildfire risks, deter illegal outdoor fire use, and detect potential fire hazards via thermal imaging sensors. \\ \cline{1-1}  & 2. Earthquake Damage Reconnaissance in Shigatse, Tibet On 7 January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse City, Tibet, with a focal depth of 10 km, causing widespread building collapse and casualties. The Department of Emergency Management of Tibet Autonomous Region urgently deployed the Wing Loong-2H emergency relief UAV to conduct a full-scale damage assessment of the epicenter area. Equipped with electro-optical devices and synthetic aperture radar (SAR), the drone successfully captured critical data, including collapsed buildings, trapped personnel locations, and relief camp layouts. It also provided stable imaging and real-time data transmission under nighttime low-visibility conditions, offering robust support for rescue operations. \\ \cline{1-1}  & 2. Spring Festival Emergency Management in Guangzhou During the 2025 Spring Festival, Guangzhou's Emergency Command Center utilized drones for real-time monitoring of the Baietan fireworks display. Using AI-powered cloud platform cameras and high-definition imaging, the drones analyzed crowd and vehicle flows, quantified densities, and promptly identified risks, such as overcrowding and traffic bottlenecks. Simultaneously, drones patrolled key forested areas, like Hulu Mountain and Baishuizhai, to monitor wildfire risks, deter illegal outdoor fire use, and detect potential fire hazards via thermal imaging sensors. \\ \hline \multirow{8}{*}{DeepSeekwith few-shot  
Table 2 demonstrates significant differences in LLMs' entity extraction performance. iFLTTEK exhibited fundamental task misalignment, generating partial semantic descriptors, like \" terrain changes in landslide areas and vegetation destruction\", rather than discrete entities. While ERNIE Bot and DeepSeek were able to comprehend basic tasks, both models suffered from contextual recognition limitations, creating composite phrases like \"forest burn areas\" instead of atomic entities. This pattern reveals challenges in isolating core semantic units from descriptive contexts, exemplified by erroneous outputs like \"fire source locations\" (contextual dependency) versus the required \"fire sources\" (atomic entities). Furthermore, inconsistent semantic granularity persisted across models, indicating difficulties in maintaining abstraction levels aligned with task specifications. To optimize LLMs-based entity extraction performance, we developed an enhanced few-shot learning protocol by supplying 10 annotated domain-specific exemplars during  
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt}} \hline \hline Input & Your task is to process all the following texts, requiring the use of [] to mark descriptions of physical targets captured by drone photography (e.g., collapsed buildings) in each paragraph. At the end of each paragraph, provide the mission context and captured entities (e.g., post-earthquake rescue \(-\)buildings, roads). \\ \hline \multirow{8}{*}{DeepSeek} & 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.