# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 3 Method
### Entity Extraction and Relationship Analysis  
During the entity extraction phase, manual annotation took place of all corpora in order to identify tasks and the entities that appear in the task entities in the UAV aerial photography task segments. This process yielded 14 distinct task categories and 155 unique entities, establishing foundational references for subsequent analysis. These manually curated results served as standard benchmarks for evaluating LLM-based entity extraction performance. Then, we instructed multiple LLMs to extract entities on raw corpora under identical task specifications. The comparative results in Table 2 reveal critical performance variations across models. \begin{table}
\begin{tabular}{p{14.2 pt} p{346.9 pt}} \hline \hline Input & Your task is to collect news reports and official announcements regarding aerial drone photography technology application in emergency rescue operations. Each entry should be presented as a separate paragraph, with each paragraph explicitly describing both (1) the mission context (e.g., post-disaster damage assessment) and (2) the physical entities captured by aerial photography (e.g., vegetation, residential buildings, etc.). \\ \hline \multirow{8}{*}{DeepSeek} & 1. Background: UAV No-Fly Zone Management During 2025 Spring Festival To ensure the safety of festival events during the Spring Festival period, a temporary no-fly zone will be established from 04:00 to 08:30 on 1 February 2025, covering areas east of Bayi Avenue and west of Guangdong South Road. This measure prohibits the operation of drones and other low-altitude, slow-speed, small-sized (LSS) aircraft to prevent aerial photography from disrupting event operations and maintain public safety. \\ \cline{1-1}  & 2. Background: Post-Disaster Assessment of Typhoon “Yagi” in Hainan Province In September 2024, Hainan Province was struck by Typhoon “Yagi.” The Seventh Topographic Survey Team of the Ministry of Natural Resources deployed four unmanned aerial vehicles (UAVs) to conduct aerial surveys of affected areas, including Haikou City and Wenchang City. The operation captured 326 photos and 55 videos that clearly documented damage to houses, wind turbines, vegetation, and aquaculture farms. This data provides a scientific basis for post-disaster recovery efforts. \\ \hline \multirow{8}{*}{ERNIE Bot} & 1. Drones Assist in Post-Disaster Damage Assessment Mission Background: A region was struck by a severe flood disaster, resulting in large areas of submerged farmland and severely damaged houses. To accurately assess disaster impact, the local emergency management department promptly initiated a drone aerial survey program. Aerial Survey Objectives: Drones conducted a comprehensive aerial survey of the affected areas, focusing on capturing submerged farmland, collapsed houses, and damaged roads and bridges. The operation provided detailed data support for subsequent damage assessment and recovery planning. \\ \hline \multirow{8}{*}{Output} & News Report 1 During the [disaster name] that occurred on [specific date], the affected areas faced severe devastation, necessitating a comprehensive post-disaster damage assessment to formulate follow-up rescue and reconstruction plans. To address this, the emergency response department swiftly deployed a team equipped with advanced drone aerial survey technology. \\ \cline{1-1}  & The drones ascended steadily and began conducting comprehensive multi-angle aerial surveys over the disaster-stricken zones. From an aerial perspective, large swathes of once-lush forests now showed extensive tree collapse. In some areas, large trees lay haphazardly on the ground with broken branches scattered widely. Landslides exposed bare soil and rocks on slopes of varying severity. In village areas, numerous houses sustained damage: some had roofs torn off, with tiles strewn around; others exhibited cracked walls or partial collapse, burying furniture and household items under debris.