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Data integration for research and innovation policy: an Ontology-Based Data Management approach

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Abstract

This paper proposes an Ontology-Based Data Management (OBDM) approach to coordinate, integrate and maintain the data needed for Science, Technology and Innovation (STI) policy development. The OBDM approach is a form of integration of information in which the global schema of data is substituted by the conceptual model of the domain, formally specified through an ontology. Implemented in Sapientia, the ontology of multi-dimensional research assessment, it offers a transparent platform as the base for the assessment process; it enables one to define and specify in an unambiguous way the indicators on which the evaluation is based, and to track their evolution over time; also it allows to the analysis of the effects of the actual use of the indicators on the behavior of scholars, and spot opportunistic behaviors; and it provides a monitoring system to track over time the changes in the established evaluation criteria and their consequences for the research system. It is argued that easier access to and a more transparent view of scientific-scholarly outcomes help to improve the understanding of basic science and the communication of research outcomes to the wider public. An OBDM approach could successfully contribute to solve some of the key issues in the integration of heterogeneous data for STI policies.

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Notes

  1. Sapientia 1.0 has been presented at the Workshop of the 20 February 2015 held at DIAG, Sapienza University of Rome whose proceedings are reported in Daraio (2015).

  2. An interesting comparison is possible with the standard setting process in the accounting community (IFRS 2015) and the development of taxonomies and formal languages like XBRL to communicate and manipulate accounting documents (IFRS 2014).

  3. Even the assessment of R&D performance in a profit oriented organization will gain in insight and generality if multiple approaches (qualitative and quantitative, micro and macro) are parallel pursued and compared (Werner and Souder 1997; Nudurupati et al. 2011).

References

  • Agarwal, R., & Dhar, V. (2014). Editorial—Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25(3), 443–448.

    Article  Google Scholar 

  • AUBR Expert Group. (2010). Expert Group on the Assessment of University-Based Research. Assessing Europe’s University-Based Research. European Commission—DG Research. EUR 24187 EN.

  • Baader, F., Calvanese, D., McGuinness, D., Nardi, D., & Patel-Schneider, P. F. (Eds.). (2007). The description logic handbook: Theory, implementation and applications (2nd ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • Bernstein, P. A., & Haas, L. (2008). Information integration in the enterprise. Communications of the ACM, 51(9), 72–79.

    Article  Google Scholar 

  • Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., & Rosati, R. (2007). Tractable reasoning and efficient query answering in description logics: The DL-Lite family. Journal of Automated Reasoning, 39(3), 385–429.

    Article  MATH  MathSciNet  Google Scholar 

  • Ceri, S., Gottlob, G., & Tanca, L. (1990). Logic programming and databases. Berlin (Germany): Springer.

    Book  Google Scholar 

  • Cronin, B. (2013). Thinking about data. Journal of the American Society for Information Science and Technology, 64(3), 435–436.

    Article  MathSciNet  Google Scholar 

  • Cronin, B., & Sugimoto, C. (Eds.). (2014). Beyond bibliometrics. Harnessing multidimensional indicators of scholarly impact. Cambridge, MA: MIT Press.

    Google Scholar 

  • Daraio, C. (Eds.). (2015). Efficiency, effectiveness and impact of research and innovation. In Proceedings of the Workshop of the 20 February 2015 DIAG, Sapienza University of Rome, Efesto Edizioni, Rome. ISBN 9788899104306.

  • Daraio, C., & Bonaccorsi, A. (2015), Beyond university rankings? Generating new indicators on universities by linking data in open platforms. Journal of the American Society for Information Science and Technology (forthcoming).

  • Daraio, C., Bonaccorsi, A., & Simar, L. (2015a). Rankings and university performance: A conditional multidimensional approach. European Journal of Operational Research, 244, 918–930.

    Article  Google Scholar 

  • Daraio, C., Lenzerini, M., Leporelli, C., Moed, H. F., Naggar, P., Bonaccorsi, A., et al. (2015b). Sapientia the ontology of multi-dimensional research assessment. In A. A. Salah, Y. Tonta, A. A. Akdag Salah, C. Sugimoto, & U. Al (Eds.), Proceedings of ISSI 2015 Istanbul: 15th International Society of Scientometrics and Informetrics Conference, Istanbul, Turkey, 29 June to 3 July, 2015, Bogaziçi University Printhouse (pp. 965–977).

  • Daraio, C., Lenzerini, M., Leporelli, C., Moed, H. F., Naggar, P., Bonaccorsi, A., et al. (2015c). Connecting big scholarly data with science of science policy: An ontology-based-data-management (Obdm) approach. In A. A. Salah, Y. Tonta, A. A. Akdag Salah, C. Sugimoto, & U. Al (Eds.), Proceedings of ISSI 2015 Istanbul: 15th International Society of Scientometrics and Informetrics Conference, Istanbul, Turkey, 29 June to 3 July, 2015, Bogaziçi University Printhouse (pp. 1232–1233).

  • Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., et al. (2015). Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66(8), 1523–1545.

    Article  Google Scholar 

  • Fealing, K. H., Lane, J. I., Marburger, J. H, I. I. I., & Shipp, S. S. (Eds.). (2011). The science of science policy, a handbook. Stanford: Stanford University Press.

    Google Scholar 

  • Frické, M. (2015). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651–661.

    Article  Google Scholar 

  • Georgescu-Roegen, N. (1970). The economics of production. The American Economic Review, 1970, 1–9.

    Google Scholar 

  • Georgescu-Roegen, N. (1972). Process analysis and the neoclassical theory of production. American Journal of Agricultural Economics, 1972, 279–294.

    Article  Google Scholar 

  • Georgescu-Roegen, N. (1979). Methods in economic science. Journal of Economic Issues, 1979, 317–328.

    Article  Google Scholar 

  • IFRS. (2014). A guide to understanding IFRS taxonomy update. IFRS taxonomy guides. http://www.ifrs.org/XBRL/IFRS-Taxonomy/2014-IFRS-15-Revenue-Contracts-Customers/Documents/GuideToUnderstandingTheIFRSTaxonomyUpdate_AUG%202014.pdf. Accessed 14 Oct 2015.

  • IFRS. (2015). Conceptual framework for financial reporting. Exposure draft ED/2015/3. http://www.ifrs.org/Current-Projects/IASB-Projects/Conceptual-Framework/Documents/May%202015/ED_CF_MAY%202015.pdf. Accessed 14 Oct 2015.

  • Imielinski, T., & Lipski, W, Jr. (1984). Incomplete information in relational databases. Journal of the ACM, 31(4), 761–791.

    Article  MATH  MathSciNet  Google Scholar 

  • Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data and Society, 1(1), 1–12.

    Article  Google Scholar 

  • Lenzerini, M. (2011). Ontology-based data management. CIKM, 2011, 5–6.

    Google Scholar 

  • Moed, H. F., & Halevi, G. (2015). The multidimensional assessment of scholarly research impact. Journal of the American Society for Information Science and Technology, 66(10), 1988–2002.

    Article  Google Scholar 

  • Nudurupati, S. S., Bititci, U. S., Kumar, V., & Chan, F. T. S. (2011). State of the art literature review on performance measurement. Computers and Industrial Engineering, 60, 279–290.

    Article  Google Scholar 

  • Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., & Rosati, R. (2008). Linking data to ontologies. Journal on Data Semantics, X, 133–173.

    Google Scholar 

  • REF (Research Excellence Framework). (2012). Panel criteria and working methods. Retrieved January 7, 2015 from: http://www.ref.ac.uk/media/ref/content/pub/panelcriteriaandworkingmethods/01_12.pdf.

  • Sarma A. D., Dong X., Alon, Y., & Halevy, A. (2008). Bootstrapping pay-as-you-go data integration systems. In Proceedings of ACM SIGMOD 2008 (pp. 861–874).

  • Werner, B. M., & Souder, W. E. (1997). Measuring R&D performance—State of the art. Research Technology Management, 40(2), 34.

    Google Scholar 

Download references

Acknowledgments

Research support from the “Progetto di Ateneo 2013 (C26A13ZXRY)” of the Sapienza university of Rome is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), Sapienza University of Rome, via Ariosto, 25, 00185, Rome, Italy

    Cinzia Daraio, Maurizio Lenzerini, Claudio Leporelli & Henk F. Moed

  2. Studiare Ltd., Rome, Italy

    Paolo Naggar & Alessandro Bartolucci

  3. Dipartimento di Ingegneria dell’Energia dei Sistemi del Territorio e delle Costruzioni (DESTEC), University of Pisa, Pisa, Italy

    Andrea Bonaccorsi

Authors
  1. Cinzia Daraio
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  2. Maurizio Lenzerini
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  3. Claudio Leporelli
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  4. Henk F. Moed
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  5. Paolo Naggar
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  6. Andrea Bonaccorsi
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  7. Alessandro Bartolucci
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Corresponding author

Correspondence to Cinzia Daraio.

Additional information

This work is based on two papers accepted for presentation and published in the proceedings of the ISSI 2015 Conference (see Daraio et al. 2015b, c).

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Daraio, C., Lenzerini, M., Leporelli, C. et al. Data integration for research and innovation policy: an Ontology-Based Data Management approach. Scientometrics 106, 857–871 (2016). https://doi.org/10.1007/s11192-015-1814-0

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