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A multi-faceted method for science classification schemes (SCSs) mapping in networking scientific resources

Author

Listed:
  • Wei Du

    (City University of Hong Kong)

  • Raymond Yiu Keung Lau

    (City University of Hong Kong)

  • Jian Ma

    (City University of Hong Kong)

  • Wei Xu

    (Renmin University of China)

Abstract

Science classification schemes (SCSs) are built to categorize scientific resources (e.g. research publications and research projects) into disciplines for effective research analytics and management. With the explosive growth of the number of scientific resources in distributed research institutions in recent years, effectively mapping different SCSs, especially heterogeneous SCSs that categorize different kinds of scientific resources, is becoming an increasingly challenging problem for facilitating information interoperability and networking scientific resources. To effectively realize the heterogeneous SCSs mapping, we design a novel multi-faceted method to measure the similarity between two classes based on three important facets, namely descriptors, individuals, and semantic neighborhood. Particularly, the proposed approach leverages a hybrid method combining statistical learning, semantic analysis and structure analysis for effective measurement with the exploitation of symmetric Tversky’s index, WordNet dictionary and the Hungarian Algorithm. The method has been evaluated based on two main SCSs that need mapping for information management and policy-making in NSFC, and shown satisfying results. The interoperability among heterogeneous SCSs is resolved to enhance the access to heterogeneous scientific resources and the development of appropriate research analytics policies.

Suggested Citation

  • Wei Du & Raymond Yiu Keung Lau & Jian Ma & Wei Xu, 2015. "A multi-faceted method for science classification schemes (SCSs) mapping in networking scientific resources," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2035-2056, December.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:3:d:10.1007_s11192-015-1742-z
    DOI: 10.1007/s11192-015-1742-z
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    References listed on IDEAS

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    Cited by:

    1. Hongbing Jiang & Chen Yang & Jian Ma & Thushari Silva & Huaping Chen, 2016. "A social voting approach for scientific domain vocabularies construction," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 803-820, August.
    2. Wei Du & Xusen Cheng & Chen Yang & Jianshan Sun & Jian Ma, 2017. "Establishing interoperability among knowledge organization systems for research management: a social network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1489-1506, September.

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