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Challenges, Approaches and Solutions in Data Integration for Research and Innovation

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Springer Handbook of Science and Technology Indicators

Part of the book series: Springer Handbooks ((SHB))

Abstract

In order to be implemented by policy makers, science, technology, and innovation ( ) policies and indicator building need data. Whenever we need data, we need a method for data management, and in the era of big data , a crucial role is played by data integration . Therefore, STI policies and indicator development need data integration. Two main approaches to data integration exist, namely procedural and declarative. In this chapter, we follow the latter approach and focus our attention on the ontology-based data integration ( ) paradigm. The main principles of OBDI are:

  1. (i)

    Leave the data where they are.

  2. (ii)

    Build a conceptual specification of the domain of interest (ontology), in terms of knowledge structures.

  3. (iii)

    Map such knowledge structures to concrete data sources.

  4. (iv)

    Express all services over the abstract representation.

  5. (v)

    Automatically translate knowledge services to data services.

We introduce the main challenges of data integration for research and innovation ( ) and show that reasoning over an ontology connected to data may be very helpful for the study of R&I. We also provide examples by using Sapientia, an ontology specifically defined for multidimensional research assessment.

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Acknowledgements

Financial support from the Project Sapienza Awards 2015 n. C26H15XNFS and the Project FILAS RU 2014-1186 is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy

    Maurizio Lenzerini & Cinzia Daraio

Authors
  1. Maurizio Lenzerini
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  2. Cinzia Daraio
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Corresponding author

Correspondence to Maurizio Lenzerini .

Editor information

Editors and Affiliations

  1. ECOOM and Faculty of Economics and Business, KU Leuven, Leuven, Belgium

    Wolfgang Glänzel

  2. Amsterdam, The Netherlands

    Henk F. Moed

  3. Competence Center Policy – Industry – Innovation, Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany

    Ulrich Schmoch

  4. Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK

    Mike Thelwall

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Lenzerini, M., Daraio, C. (2019). Challenges, Approaches and Solutions in Data Integration for Research and Innovation. In: Glänzel, W., Moed, H.F., Schmoch, U., Thelwall, M. (eds) Springer Handbook of Science and Technology Indicators. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-02511-3_15

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