Proceedings of the 2nd International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2023, July 7–9, 2023, Chongqing, China

Research Article

Research on the Application of Knowledge Graph in Academic Resource Discovery Service

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  • @INPROCEEDINGS{10.4108/eai.7-7-2023.2338061,
        author={Jianfeng  Zhou and Weiming  Yang and Jing  Ma},
        title={Research on the Application of Knowledge Graph in Academic Resource Discovery Service},
        proceedings={Proceedings of the 2nd International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2023, July 7--9, 2023, Chongqing, China},
        publisher={EAI},
        proceedings_a={FFIT},
        year={2023},
        month={10},
        keywords={university library; knowledge map; academic resources; discovery; individuation},
        doi={10.4108/eai.7-7-2023.2338061}
    }
    
  • Jianfeng Zhou
    Weiming Yang
    Jing Ma
    Year: 2023
    Research on the Application of Knowledge Graph in Academic Resource Discovery Service
    FFIT
    EAI
    DOI: 10.4108/eai.7-7-2023.2338061
Jianfeng Zhou1,*, Weiming Yang1, Jing Ma1
  • 1: Guangdong University of Foreign Studies
*Contact email: 201110082@oamail.gdufs.edu.cn

Abstract

[Objective] To apply user graph to the service process of academic resource discovery, alleviate the dilemma of knowledge loss, and provide reference for the construction and application of user graph in university libraries. [Process] Taking academic resource discovery service as the application scenario, this paper analyzes the source, structure and characteristics of university library user data, and defines the user graph construction process of "knowledge extraction - knowledge fusion - knowledge snapshot - graph tailoring". The project of embedding user graph into academic resource discovery service process was discussed in three stages: before, during and after, and user behavior data was used for analysis and testing. [Results] The experiment proved that user graph can optimize the input and output of academic resource discovery service, reduce the cost of user behavior, and provide a reference for the application of knowledge graph.