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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).

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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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