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The advantages of an Ontology-Based Data Management approach: openness, interoperability and data quality

Abstract

We illustrate the usefulness of an Ontology-Based Data Management (OBDM) approach to develop an open information system, allowing for a deep level of interoperability among different databases, and accounting for additional dimensions of data quality compared to the standard dimensions of the OECD (Quality framework and guidelines for OECD statistical activities, OECD Publishing, Paris, 2011) Quality Framework. Recent advances in engineering in computer science provide promising tools to solve some of the crucial issues in data integration for Research and Innovation.

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Notes

  1. According to OECD (2015), open data are “data that can be used by anyone without technical or legal restrictions. The use encompasses both access and reuse.” OECD (2015, p. 7).

  2. According to OECD (2015), open science refers to “efforts by researchers, governments, research funding agencies or the scientific community itself to make the primary outputs of publicly funded research results—publications and the research data—publicly accessible in digital format with no or minimal restriction as a means for accelerating research; these efforts are in the interest of enhancing transparency and collaboration, and fostering innovation. […] Three main aspects of open science are: open access, open research data, and open collaboration enabled through ICTs. Other aspects of open science—post-publication peer review, open research notebooks, open access to research materials, open source software, citizen science, and research crowdfunding are also part of the architecture of an open science system” (OECD 2015, p. 7).

  3. An automated reasoner based on logic is a software which is able to derive logical consequences from a given set of axioms in an automatic way.

  4. This presentation follows the lines of Calvanese et al. (2011) and Civili et al. (2013).

  5. SPARQL is a semantic query language for databases.

  6. Sapientia 1.0 was closed on the 22nd of December 2014, and was organized in 14 Modules, including around 350 symbols (concepts, relations and attributes). It has been presented at the Workshop of the 20 February 2015 held at Sapienza University of Rome (see Daraio 2015).

References

  • 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.

    MATH  Google Scholar 

  • Baldwin, C. Y., & Clark, K. (2000). Design rules—The power of modularity. Cambridge: MIT Press.

    Google Scholar 

  • Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. Cambridge: MIT Press.

    Google Scholar 

  • Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., & Rosati, R. (2009a). Ontologies and databases: The DL-Lite approach. In S. Tessaris, E. Franconi, T. Eiter, C. Gutierrez, S. Handschuh, M.-C. Rousset & R. A. Schmidt (Eds.), Reasoning Web. Semantic Technologies for Information Systems, Lecture Notes in Computer Science (Vol. 5689, pp. 255–356). Berlin: Springer.

  • Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., & Rosati, R. (2009b). Ontology-based data access and integration. Encyclopedia of database systems. Berlin: Springer.

    MATH  Google Scholar 

  • Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., et al. (2011). The Mastro system for ontology-based data access. Semantic Web, 2(1), 43–53.

    Google Scholar 

  • Civili, C., Console, M., De Giacomo, G., Lembo, D., Lenzerini, M., Lepore, L., & Santarelli, V. (2013). Mastro studio: Managing ontology-based data access applications. Proceedings of the VLDB Endowment, 6(12), 1314–1317.

    Article  Google Scholar 

  • Console, M., & Lenzerini, M. (2014). Data quality in ontology-based data access: The case of consistency. AAAI, 2014, 1020–1026.

    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., Lenzerini, M., Leporelli, C., Moed, F. H., Naggar, P., Bonaccorsi, A., & Bartolucci, A. (2016). Data integration for research and innovation policy: An ontology-based data management approach. Scientometrics, 106(2), 857–871.

    Article  Google Scholar 

  • European Commission (2010). Communication from the commission to the European parliament, the council, the European economic and social committee and the committee of the regions. A digital agenda for Europe, Brussels. COM(2010)245 final. Available at: http://eur-lex.europa.eu/legalcontent/EN/TXT/PDF/?uri=CELEX:52010DC0245&from=EN. Accessed 19 May 2010.

  • Floridi, L. (2014). The fourth revolution: How the infosphere is reshaping human reality. Oxford: OUP Oxford.

    Google Scholar 

  • Hanson, B., Sugden, A., & Alberts, B. (2011). Making data maximally available. Science, 331(6018), 649.

    Article  Google Scholar 

  • Hilbert, M., & López, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.

    Article  Google Scholar 

  • Huijboom, N., & Van den Broek, T. (2011). Open data: An international comparison of strategies. European Journal of ePractice, 12(1), 4–16.

    Google Scholar 

  • Kshetri, N. (2014). Big data's impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134–1145.

    Article  Google Scholar 

  • Lenzerini, M. (2002). Data integration: A theoretical perspective. PODS, 2002, 233–246.

    Google Scholar 

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

  • Li, X., & Johnson, J. D. (2002). Evaluate IT investment opportunities using real options theory. Information Resources Management Journal, 15(3), 32–47.

    Article  Google Scholar 

  • Moed, H. F. (2016). Altmetrics as traces of the computerization of the research process. In C. R. Sugimoto (Ed.), Theories of informetrics and scholarly communication. A Festschrift in honor of Blaise Cronin (pp. 360–371). Berlin: De Gruyter.

    Google Scholar 

  • National Research Council. (2004). Open access and the public domain in digital data and information for science: Proceedings of an international symposium. Washington, DC: The National Academies Press.

  • National Research Council. (2012). The case for international sharing of scientific data: A focus on developing countries. Washington, D.C.: National Academies Press.

    Google Scholar 

  • Nielsen, M. (2012). Reinventing discovery: The new era of networked science. Princeton: Princeton University Press.

    Google Scholar 

  • OECD. (2011). Quality framework and guidelines for OECD statistical activities. Paris: OECD Publishing.

    Google Scholar 

  • OECD. (2015). Making open science a reality. OECD science, technology and industry policy papers no. 25. Paris: OECD Publishing. http://dx.doi.org/10.1787/5jrs2f963zs1-en.

  • Parent, C., & Spaccapietra, S. (2000). Database integration: The key to data interoperability. In M. P. Papazoglou & Z. Zari (Eds.), Advances in object-oriented data modeling (pp. 221–253). Cambridge: The MIT press.

    Google Scholar 

  • Parnas, D. L. (1972). On the criteria to be used in decomposing systems into modules. Communications of The ACM, 15(12), 1053–1058.

    Article  Google Scholar 

  • Pinfield, S., Salter, J., Bath, P. A., Hubbard, B., Millington, P., Anders, J. H., & Hussain, A. (2014). Open access repositories worldwide, 2005–2012: Past growth, current characteristics, and future possibilities. Journal of the Association for Information Science and Technology, 65(12), 2404–2421.

    Article  Google Scholar 

  • Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., & Rosati, R. (2008). Linking data to ontologies. In S. Spaccapietra (Ed.), Journal on Data Semantics X, Lecture Notes in Computer Science (Vol. 4900, pp. 133–173). Berlin: Springer.

  • Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106, 467–482.

    Google Scholar 

  • Tolk, A., Muguira, J. A. (2003). The levels of conceptual interoperability model. In Proceedings of the 2003 fall simulation interoperability workshop (Vol. 7).

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Acknowledgments

The helpful and precious comments and suggestions of Henk F. Moed are warmly acknowledged. Research support from the Award Project 2015 No. C26H15XNFS of the Sapienza university of Rome is gratefully acknowledged.

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Correspondence to Cinzia Daraio.

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Daraio, C., Lenzerini, M., Leporelli, C. et al. The advantages of an Ontology-Based Data Management approach: openness, interoperability and data quality. Scientometrics 108, 441–455 (2016). https://doi.org/10.1007/s11192-016-1913-6

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Keywords

  • Data integration
  • Open data
  • Comparability
  • Standardization
  • Modularization
  • Interoperability
  • Data quality
  • Research and innovation