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2024 , Vol. 68 >Issue 3: 105 - 116

DOI: https://doi.org/10.13266/j.issn.0252-3116.2024.03.010

KNOWLEDGE ORGANIZATION

Construction and Experimental Exploration of Domain Knowledge Graph for Exploratory Search

  • Wang Juan ,
  • Cao Shujin ,
  • Wang Zhihong ,
  • Peng Bitao
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  • 1 School of Information Science and Technology, Guangdong University of Foreign Studies, Guangdong 510420;
    2 Institute of Information Management, Shandong University of Technology, Zibo 255000;
    3 Zhongguancun Laboratory, Beijing 102206

Received date: 2023-06-19

  Revised date: 2023-08-18

  Online published: 2024-02-23

Supported by

This work is supported by the National Social Science Fund of China titled "Research on Network Academic Information Discovery in Subject Fields Based on Deep Learning" (Grant No. 18BTQ065).

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Abstract

[Purpose/Significance] Information retrieval systems in specific domains often suffer from low retrieval efficiency due to the users’ lack of domain knowledge. Existing domain-specific knowledge organization tools, such as domain knowledge graphs, can effectively alleviate this problem, but how to better integrate them with existing information retrieval systems is a problem that has not been solved yet. Therefore, this paper proposes a full-process solution for implementing an exploratory search system based on domain knowledge graphs, including joint extraction of entities and their relations, domain knowledge graph construction and implementation of exploratory search system. [Method/Process] This paper focused on the academic literature in library and information science (LIS) and explored the formal definition of entities and their relations in LIS. It also proposed a joint extraction method of entities and relations on Paddle UIE, and built a prototype retrieval system on an interactive LIS knowledge graph. [Result/Conclusion] By comparing the effects of different joint extraction methods of entities and relations, including Paddle UIE, CasRel, SpERT and CORE, it finds that the pre-trained large language model Paddle UIE based on prompt learning has better extraction performance, especially in the case of small sample size. In addition, this paper further designs two search experiment tasks to analyze the effect of exploratory search system. Compared with general search engines, this system can significantly improve user satisfaction and effectively solve the problem of low retrieval performance due to insufficient user domain knowledge (such as cross-disciplinary scenarios), indicating that the full-process solution proposed in this paper can provide process guidance and suggestions for academic literature service providers to develop user support tools.

Key words： domain knowledge graph; prompt learning; large language model; joint extraction; exploratory search

Cite this article

Wang Juan , Cao Shujin , Wang Zhihong , Peng Bitao . Construction and Experimental Exploration of Domain Knowledge Graph for Exploratory Search[J]. Library and Information Service, 2024 , 68(3) : 105 -116 . DOI: 10.13266/j.issn.0252-3116.2024.03.010

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