# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 1 Introduction  
Semantic communication has emerged as a pivotal enabling technology for 6G networks, demonstrating significant advantages in reducing transmission loads and enhancing service quality [1]. Unlike conventional syntactic systems focusing on bit-level accuracy, semantic communication focus on the meaning of information. This paradigm shift relies on knowledge-driven frameworks, where semantic analysis is performed against background knowledge. Recent studies confirm that semantic knowledge bases (SKBs) serve as essential infrastructure for formalizing task information and domain expertise in semantic communication systems [2, 3, 4, 5]. Especially in task-oriented scenarios, accurate and complete SKBs are crucial to efficient semantic interactions. Consequently, the rapid construction of task SKBs constitutes a critical research challenge in optimizing task-driven semantic communication architectures. Traditional knowledge graph construction methods exhibit three fundamental limitations in semantic communication applications [6]: (1) labor-intensive manual annotation requiring domain specialists, (2) rigid schema design dependent on prior expertise, (3) scalability constraints caused by iterative quality assurance processes. Typically, these methods require domain experts, creating bottlenecks in dynamic environments. For knowledge-sensitive semantic communication scenarios, such constraints hinder rapid knowledge base deployment. Consequently, there is an urgent need to streamline the process of constructing knowledge graphs to meet semantic communication requirements. The rapid development of large language models (LLMs), particularly the GPT series and DeepSeek, offers transformative capabilities for knowledge engineering [7]. These models exhibit three core strengths: natural language understanding, cross-domain reasoning, and representation learning. Such capabilities enable the automated execution of key tasks, including corpus collection, entity extraction, and relationship mapping [8]. Furthermore, prompt engineering allows precise model fine-tuning to enhance task-specific accuracy [9]. These advantages establish LLMs as foundational tools for reconstructing knowledge base development in semantic communication systems. Therefore, it is believed that LLMs can be adopted to quickly complete knowledge collation, entity extraction, and knowledge graph construction for specific tasks. Designing a knowledge graph construction scheme based on LLMs that integrates knowledge organization, entity extraction, and graph generation will advance the development of task-driven semantic communication technologies. This paper aims to design a knowledge graph construction based on LLMs and improve the efficiency and accuracy through prompt engineering and few-shot learning techniques. The principal contributions of this study comprise three key aspects:  
1. An LLM-driven framework is developed to construct task SKBs, and entity extraction accuracy is improved by using few-shot learning technology. With this approach, task knowledge bases in semantic communication systems can be built without the high dependence on manual annotation and domain expertise found in traditional knowledge graph construction methods. 2. The knowledge base for UAV aerial photography tasks is designed, which provides basic support for the application of task-oriented semantic communication technology in UAV aerial photography scenes. This advancement is expected to advance the adoption of semantic communication in drone communications, addressing critical challenges, such as bandwidth resource constraints, in drone communication systems. 3. In the experimental phase, this study tested multiple LLMs. The results demonstrate that all LLMs successfully accomplished entity extraction and relationship analysis tasks with robust performance. Especially, DeepSeek provided authentic and valid corpus data, significantly enhancing knowledge base construction reliability.