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Performance Evaluation of Re-ranking and Query Expansion for Citation Metrics: Based on Citation Index Databases

  • Journal of the Korean Society for Library and Information Science
  • 2023, 57(3), pp.249-277
  • DOI : 10.4275/KSLIS.2023.57.3.249
  • Publisher : 한국문헌정보학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : July 22, 2023
  • Accepted : August 12, 2023
  • Published : August 31, 2023

Hyekyung Lee 1 LeeYong-Gu 2

1경북대학교 문헌정보학과
2경북대학교

Excellent Accredited

ABSTRACT

The purpose of this study is to explore the potential contribution of citation metrics to improving the search performance of citation index databases. To this end, the study generated ten queries in the field of library and information science and conducted experiments based on the relevance assessment using 3,467 documents retrieved from the Web of Science and 60,734 documents published in 85 SSCI journals in the field of library and information science from 2000 to 2021. The experiments included re-ranking of the top 100 search results using citation metrics and search methods, query expansion experiments using vector space model retrieval systems, and the construction of a citation-based re-ranking system. The results are as follows: 1) Re-ranking using citation metrics differed from Web of Science’s performance, acting as independent metrics. 2) Combining query term frequencies and citation counts positively affected performance. 3) Query expansion generally improved performance compared to the vector space model baseline. 4) User-based query expansion outperformed system-based. 5) Combining citation counts with suitability documents affected ranking within top suitability documents.

Citation status

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