Article Details

Abstract

This research proposes an employment guidance Question-and-Answer (Q&A) system based on Knowledge Graph Retrieval-Augmented Generation (RAG). The system achieves intelligent career guidance Q&A through three key technological innovations. Firstly, it automatically constructs a structured career Knowledge Graph using Large Language Models. Secondly, it introduces a two-step hybrid strategy of “mapping transformation followed by categorical alignment” to calculate entity similarity and merge similar entities, effectively addressing issues of “homonymy” and “synonymy”. Finally, it integrates structured knowledge from the knowledge graph into the generation process of Large Language Model via RAG, thereby optimizing answers with knowledge-driven enhancements. Experimental results demonstrate that the system significantly improves the professionalism and reliability in career guidance across areas such as job understanding, skill requirements, and career development. This research provides a new technical pathway and practical paradigm for building an intelligent career service system.

Keywords

Knowledge Graph; Retrieval-Augmented Generation; employment guidance; Q&A system

References

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About the authors

Zhang Ling (1985-), female, Han ethnicity, from Wuji, Hebei, senior engineer, master's student, research direction: application of artificial intelligence technology; Chu Zhe (1984-), male, Han ethnicity, from Zaozhuang, Shandong, lecturer, master's student, research direction: application of big data technology.