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A hybrid strategy to extract metadata from scholarly articles by utilizing support vector machine and heuristics

Author

Listed:
  • Muhammad Waqas

    (Capital University of Science and Technology)

  • Nadeem Anjum

    (Capital University of Science and Technology)

  • Muhammad Tanvir Afzal

    (Shifa Tameer-e-Millat University)

Abstract

The immense growth in online research publications has attracted the research community to extract valuable information from scientific resources by exploring online digital libraries and publishers’ websites. The metadata stored in a machine comprehendible form can facilitate a precise search to enlist most related articles by applying semantic queries to the document’s metadata and the structural elements. The online search engines and digital libraries offer only keyword-based search on full-body text, which creates excessive results. The research community in recent years has adopted different approaches to extract structural information from research documents. We have distributed the content of an article into two logical layouts and metadata levels. This strategy has given our technique an advantage over the state-of-the-art (SOTA) extracting metadata with diversified publication styles. The experimental results have revealed that the proposed approach has shown a significant gain in performance of 20.26% to 27.14%.

Suggested Citation

  • Muhammad Waqas & Nadeem Anjum & Muhammad Tanvir Afzal, 2023. "A hybrid strategy to extract metadata from scholarly articles by utilizing support vector machine and heuristics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4349-4382, August.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:8:d:10.1007_s11192-023-04774-7
    DOI: 10.1007/s11192-023-04774-7
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