DOAJ Open Access 2025

A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication

Chang Guo Jiaqi Liu Wei Gao Zhenhai Lu Yao Li +2 lainnya

Abstrak

This study presents a knowledge graph construction scheme leveraging large language models (LLMs) for task-oriented semantic communication systems. The proposed methodology systematically addresses four critical stages: corpus collection, entity extraction and relationship analysis, knowledge base generation, and dynamic updating mechanisms. It is worth noting that prompt engineering is combined with few-shot learning to enhance reliability and accuracy in this methodology. Experimental demonstration showed that this methodology had superior entity extraction performance, achieving 89.7% precision and 92.3% recall rate. This scheme overcomes the demand for domain knowledge and the labor cost of traditional knowledge base construction schemes. It greatly improves the construction efficiency of knowledge graphs. This paper provides an efficient and reliable task knowledge base construction scheme for task-oriented semantic communication, which is expected to promote its wider application.

Topik & Kata Kunci

Technology Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry

Penulis (7)

Chang Guo

Jiaqi Liu

Wei Gao

Zhenhai Lu

Yao Li

Chengyuan Wang

Jungang Yang

Format Sitasi

APA MLA BibTeX

Guo, C., Liu, J., Gao, W., Lu, Z., Li, Y., Wang, C. et al. (2025). A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication. https://doi.org/10.3390/app15084575

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.3390/app15084575
Akses: Open Access ✓