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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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    1. Chaim Zins & Plácida L.V.A.C. Santos, 2011. "Mapping the knowledge covered by library classification systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(5), pages 877-901, May.
    2. Tijssen, Robert J. W., 1992. "A quantitative assessment of interdisciplinary structures in science and technology: Co-classification analysis of energy research," Research Policy, Elsevier, vol. 21(1), pages 27-44, February.
    3. van Eck, N.J.P. & Waltman, L., 2009. "How to Normalize Co-Occurrence Data? An Analysis of Some Well-Known Similarity Measures," ERIM Report Series Research in Management ERS-2009-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    4. E. C. M. Noyons & H. F. Moed & A. F. J. Raan, 1999. "Integrating research performance analysis and science mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 46(3), pages 591-604, November.
    5. Fei-Cheng Ma & Peng-Hui Lyu & Qiang Yao & Lan Yao & Shi-Jing Zhang, 2014. "Publication trends and knowledge maps of global translational medicine research," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 221-246, January.
    6. Marcia Lei Zeng & Lois Mai Chan, 2004. "Trends and issues in establishing interoperability among knowledge organization systems," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(5), pages 377-395, March.
    7. Ismael Rafols & Loet Leydesdorff, 2009. "Content‐based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(9), pages 1823-1835, September.
    8. Kevin W. Boyack & Richard Klavans & Katy Börner, 2005. "Mapping the backbone of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(3), pages 351-374, August.
    9. Chyi-Kwei Yau & Alan Porter & Nils Newman & Arho Suominen, 2014. "Clustering scientific documents with topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 767-786, September.
    10. Ed Noyons, 2001. "Bibliometric mapping of science in a policy context," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(1), pages 83-98, January.
    11. W. Glänzel & A. Schubert & H. -J. Czerwon, 1999. "An item-by-item subject classification of papers published in multidisciplinary and general journals using reference analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 44(3), pages 427-439, March.
    12. Wolfgang Glänzel & András Schubert, 2003. "A new classification scheme of science fields and subfields designed for scientometric evaluation purposes," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(3), pages 357-367, March.
    13. Chaim Zins & Plácida L.V.A.C. Santos, 2011. "Mapping the knowledge covered by library classification systems," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(5), pages 877-901, May.
    14. Nees Jan van Eck & Ludo Waltman, 2009. "How to normalize cooccurrence data? An analysis of some well‐known similarity measures," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1635-1651, August.
    15. Pim Vugteveen & Rob Lenders & Peter Van den Besselaar, 2014. "The dynamics of interdisciplinary research fields: the case of river research," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 73-96, July.
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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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