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Establishing interoperability among knowledge organization systems for research management: a social network approach

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Abstract

Knowledge organization systems (KOS) have long been established as a tool to represent organized interpretation of knowledge structures. Various types of KOS such as discipline tree for research projects, subject categories for research publications, and classifications schemes for research patents have been constructed and widely used in R&D contexts. However, the incompatible KOS, together with information proliferation in the Big Data Era, impose great challenges for effective research management. How to facilitate interoperability among heterogeneous research information sources is an important problem to be solved. KOS mapping methods were proposed to provide alternative subject access by establishing equivalence or partial equivalence relationships between classes in different KOS but suffered from “lagging mapping” and “deficient mapping”. This research proposes a social network approach that leverages online social platform information (i.e., research activities and social activities) for KOS mapping. The underlying assumption behind the approach is that “two classes/terms in different KOS are related if their corresponding research objects are connected to similar researchers”. The social network approach leverages social network analysis instead of semantic and structure analysis of metadata information for mapping degree calculation. The approach has been implemented on the largest research social platform in China and successfully realizes mapping between KOS for research management purpose. We conducted mapping between National Natural Science Foundation of China discipline tree and Web of Science subject categories in this study to examine the performance of social network approach.

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References

  • Al-Maskari, A., Sanderson, M., & Clough, P. (2007). The relationship between IR effectiveness measures and user satisfaction. In Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval: ACM (pp. 773–774).

  • Antonellis, I., Molina, H. G., & Chang, C. C. (2008). Simrank++: Query rewriting through link analysis of the click graph. Proceedings of the VLDB Endowment, 1(1), 408–421.

    Article  Google Scholar 

  • Arch-int, N., & Arch-int, S. (2013). Semantic ontology mapping for interoperability of learning resource systems using a rule-based reasoning approach. Expert Systems with Applications, 40(18), 7428–7443.

    Article  MATH  Google Scholar 

  • Avesani, P., Giunchiglia, F., & Yatskevich, M. (2005). A large scale taxonomy mapping evaluation. Technical report.

  • Barabási, A.-L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3), 590–614.

    Article  MathSciNet  MATH  Google Scholar 

  • Bellahsene, Z., Bonifati, A., & Rahm, E. (2011). Schema matching and mapping. Berlin: Springer.

    Book  MATH  Google Scholar 

  • Binding, C., & Tudhope, D. (2016). Improving interoperability using vocabulary linked data. International Journal on Digital Libraries, 17(1), 5–21.

    Article  Google Scholar 

  • Chan, L. M., & Zeng, M. L. (2002). Ensuring interoperability among subject vocabularies and knowledge organization schemes: A methodological analysis. Ifla Journal, 28(5–6), 323–327.

  • Chaplan, M. A. (1995). Mapping “Laborline thesaurus” terms to Library of Congress subject headings: Implications for vocabulary switching. The Library Quarterly, 65, 39–61.

    Article  Google Scholar 

  • Cicchetti, D. V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6(4), 284.

    Article  Google Scholar 

  • Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213.

    Article  Google Scholar 

  • Conroy, C., O'Sullivan, D., & Lewis, D. (2007). A ‘tagging’ approach to ontology mapping. In International Conference on Ontology Matching (pp. 311–315). CEUR-WS.org.

  • Du, W., Lau, R. Y. K., Ma, J., & Xu, W. (2015). A multi-faceted method for science classification schemes (Scss) mapping in networking scientific resources. Scientometrics, 105(3), 2035–2056.

    Article  Google Scholar 

  • Feng, B., Ma, J., & Fan, Z.-P. (2011). An integrated method for collaborative R&D project selection: Supporting innovative research teams. Expert Systems with Applications, 38(5), 5532–5543.

    Article  Google Scholar 

  • He, G., Feng, H., Li, C., & Chen, H. (2010). Parallel Simrank computation on large graphs with iterative aggregation. In Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining: ACM (pp. 543–552).

  • Hodge, G. (2000). Systems of knowledge organization for digital libraries: Beyond traditional authority files. In Digital Library Federation, Council on Library and Information Resources.

  • Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), 20(4), 422–446.

    Article  Google Scholar 

  • Jeh, G., & Widom, J. (2002). Simrank: A measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining: ACM (pp. 538–543).

  • Jiang, H., Yang, C., Ma, J., Silva, T., & Chen, H. (2016). A social voting approach for scientific domain vocabularies construction. Scientometrics , 108(2), 803–820.

  • Kalfoglou, Y., & Schorlemmer, M. (2003). Ontology mapping: The state of the art. The Knowledge Engineering Review, 18(01), 1–31.

    Article  MATH  Google Scholar 

  • Karagiannis, T., Le Boudec, J.-Y., & Vojnović, M. (2010). Power law and exponential decay of intercontact times between mobile devices. IEEE Transactions on Mobile Computing, 9(10), 1377–1390.

    Article  Google Scholar 

  • Knechtel, M. (2008). Access rights and collaborative ontology integration for reuse across security domains. In ESWC 2008 Ph.D. Symposium (p. 36).

  • Lei Zeng, M., & Mai Chan, L. (2004). Trends and issues in establishing interoperability among knowledge organization systems. Journal of the American Society for Information Science and Technology, 55(5), 377–395.

    Article  Google Scholar 

  • Li, L., Li, C., Chen, H., & Du, X. (2013). Mapreduce-based SimRank computation and its application in social recommender system. In 2013 IEEE international congress on big data (BigData Congress), IEEE (pp. 133–140).

  • Liu, X., Wang, G. A., Johri, A., Zhou, M., & Fan, W. (2014). Harnessing global expertise: A comparative study of expertise profiling methods for online communities. Information Systems Frontiers, 16(4), 715–727.

    Article  Google Scholar 

  • Lupton, D. (2014). ‘Feeling better connected’: Academics’ use of social media. Canberra: News & Media Research Centre, University of Canberra.

    Google Scholar 

  • Mayr, P., Tudhope, D., Clarke, S. D., Zeng, M. L., & Lin, X. (2016). Recent applications of knowledge organization systems: Introduction to a special issue. International Journal on Digital Libraries, 17(1), 1–4.

    Article  Google Scholar 

  • Noorden, R. V. (2014). Online collaboration: Scientists and the social network. Nature, 512(7513), 126–129.

  • Noorden, R. V. (2016). China by the numbers. Nature, 534(7608), 452.

  • Olson, T., & Strawn, G. (1997). Mapping the LCSH and MeSH systems. Information Technology and Libraries, 16(1), 5–19.

    Google Scholar 

  • Omelayenko, B. (2002). Integrating vocabularies: Discovering and representing vocabulary maps. In I. Horrocks & J. Hendler (Eds.), International Semantic Web Conference (Vol. 2342, pp. 206–220). Berlin: Springer.

  • Pfeffer, M. (2014). Using clustering across union catalogues to enrich entries with indexing information. In M. Spiliopoulou, L. Schmidt-thieme & R. Janning (Eds.), Data analysis, machine learning and knowledge discovery. Cham: Springer.

  • Silva, T., Guo, Z., Ma, J., Jiang, H., & Chen, H. (2013). A social network-empowered research analytics framework for project selection. Decision Support Systems, 55(4), 957–968.

  • Su, H.-N., & Lee, P.-C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in technology foresight. Scientometrics, 85(1), 65–79.

    Article  Google Scholar 

  • Sun, Y., & Han, J. (2013). Mining heterogeneous information networks: A structural analysis approach. ACM SIGKDD Explorations Newsletter, 14(2), 20–28.

    Article  Google Scholar 

  • Sun, Y., Han, J., Yan, X., Yu, P. S., & Wu, T. (2011). Pathsim: Meta path-based top-K similarity search in heterogeneous information networks. In VLDB’11.

  • Sun, J., Ma, J., Liu, Z., & Miao, Y. (2013). Leveraging content and connections for scientific article recommendation in social computing contexts. The Computer Journal , 57(9), 1331–1342.

  • Sun, J., Xu, W., Ma, J., & Sun, J. (2015). Leverage RAF to find domain experts on research social network services: A big data analytics methodology with MapReduce framework. International Journal of Production Economics, 165, 185–193.

    Article  Google Scholar 

  • Todorov, K. (2009). Detecting ontology mappings via descriptive statistical methods. In Fourth international conference on internet and web applications and services, 2009. ICIW’09. IEEE (pp. 177–182).

  • Xu, Y., Guo, X., Hao, J., Ma, J., Lau, R. Y. K., & Xu, W. (2012). Combining social network and semantic concept analysis for personalized academic researcher recommendation. Decision Support Systems, 54(1), 564–573.

    Article  Google Scholar 

  • Xu, W., Sun, J., Ma, J., & Du, W. (2016). A personalized information recommendation system for R&D project opportunity finding in big data contexts. Journal of Network and Computer Applications, 59, 362–369.

    Article  Google Scholar 

  • Yin, X., Han, J., & Yu, P. S. (2006). Linkclus: Efficient clustering via heterogeneous semantic links. In Proceedings of the 32nd international conference on very large data bases: VLDB Endowment (pp. 427–438).

  • Zhang, Y., Peng, J., Huang, D., & Li, F. (2011a). Design of automatic mapping system between DDC and CLC. In International conference on asian digital libraries (pp. 357–366). Berlin: Springer.

  • Zhang, Y., Peng, J., Huang, D., & Li, F. (2011b). Design of automatic mapping system between DDC and CLC. In Digital libraries: For cultural heritage, knowledge dissemination, and future creation (pp. 357–366). Berlin: Springer.

  • Zhdanova, A. V. (2005). Towards a community-driven ontology matching. In Proceedings of the 3rd international conference on Knowledge capture: ACM (pp. 221–222).

  • Zhdanova, A. V. (2008). Community-driven ontology construction in social networking portals. Web Intelligence and Agent Systems: An International Journal, 6(1), 93–121.

    Google Scholar 

  • Zhdanova, A. V., Krummenacher, R., Henke, J., & Fensel, D. (2005). Community-driven ontology management: Deri case study. In The 2005 IEEE/WIC/ACM international conference on web intelligence, 2005. Proceedings. IEEE (pp. 73–79).

  • Zhdanova, A. V., & Shvaiko, P. (2006). Community-driven ontology matching. Berlin: Springer.

    Book  Google Scholar 

  • Zhou, D., Orshanskiy, S., Zha, H., & Giles, C. L. (2007). Co-ranking authors and documents in a heterogeneous network. In Seventh IEEE international conference on data mining, 2007. ICDM 2007. IEEE (pp. 739–744).

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Acknowledgements

The authors gratefully thank the Editor and all reviewers. The authors also acknowledge with gratitude the generous support of National Natural Science Foundation of China (M1552003, 71371164), CityU Research Grant (9620366, 7004715) and Humanity and Social Science Youth Foundation of Ministry of Education of China (16YJC630153).

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, China

    Wei Du

  2. School of Information Technology and Management, University of International Business and Economics, Beijing, China

    Xusen Cheng

  3. College of Management, Shenzhen University, Shenzhen, China

    Chen Yang

  4. School of Management, Hefei University of Technology, Hefei, China

    Jianshan Sun

  5. Department of Information Systems, College of Business, City University of Hong Kong, Kowloon, Hong Kong

    Jian Ma

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  1. Wei Du
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  2. Xusen Cheng
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Correspondence to Wei Du.

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Du, W., Cheng, X., Yang, C. et al. Establishing interoperability among knowledge organization systems for research management: a social network approach. Scientometrics 112, 1489–1506 (2017). https://doi.org/10.1007/s11192-017-2457-0

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