Reconciling Practice and Rigour in Ontology-Based Heterogeneous Information Systems Construction

  • Carme QuerEmail author
  • Xavier Franch
  • Cristina Palomares
  • Andreas Falkner
  • Alexander Felfernig
  • Davide Fucci
  • Walid Maalej
  • Jennifer Nerlich
  • Mikko Raatikainen
  • Gottfried Schenner
  • Martin Stettinger
  • Juha Tiihonen
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 335)


Ontology integration addresses the problem of reconciling into one single semantic framework different knowledge chunks defined according to its own ontology. This field has been subject of analysis and many consolidated theoretical results are available. Still, in practice, ontology integration is difficult in heterogeneous information systems (HIS) that need to integrate assets already built and running which cannot be changed. Furthermore, in practice, the composed assets are usually not really defined according to an ontology but to a data model which is less rigorous but fit for the purpose of defining a data schema. In this paper, we propose a method for integrating assets participating in a HIS using a domain ontology, aimed at finding an optimal balance between semantic rigour and feasibility in terms of adoption in a real-world setting. The method proposes the use of data models describing the semantics of existing assets; their analysis in order to find commonalities and misalignments; the definition of the domain ontology, considering also other sources as standards, to express the main concepts in the HIS domain; the connection of the local models with this domain ontology; and its abstraction into a metamodel to facilitate further extensions. The method is an outcome of a collaborative software development project, OpenReq, aimed at delivering an ontology for requirements engineering (RE) designed to serve as baseline for the data model of an open platform offering methods and techniques to the RE community. The construction process of this ontology will be used to illustrate the method.


Ontology integration Heterogeneous information systems Domain ontology Requirements engineering ontology 



This work is a result of the OpenReq project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732463.


  1. 1.
    Gruber, T.R.: A translation approach to portable ontologies. Knowl. Acquis. 5(2), 199–229 (1993)Google Scholar
  2. 2.
    Guizzardi, G.: On ontology, ontologies, conceptualizations, modeling languages, and (meta) models. Front. Artif. Intell. Appl. 155, 18 (2007)Google Scholar
  3. 3.
    Wache, H., Voegele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., & Hübner, S.: Ontology-based integration of information-a survey of existing approaches. In: IJCAI-01 Workshop: Ontologies and Information Sharing (2001)Google Scholar
  4. 4.
    Sowa, J.F.: Building, Sharing, and Merging Ontologies (2001).
  5. 5.
    Calvanese, D., de Giacomo, G., Lenzerini, M.: Ontology of integration and integration of ontologies. In: DL Workshop 2001 – CEUR 49 (2001)Google Scholar
  6. 6.
    De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rosati, R.: Using ontologies for semantic data integration. In: Flesca, S., Greco, S., Masciari, E., Saccà, D. (eds.) A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years. SBD, vol. 31, pp. 187–202. Springer, Cham (2018). Scholar
  7. 7.
    Visser, P.R.S., Jones, D.M., Bench-Capon, T.J.M., Shave, M.J.R.: An analysis of ontology mismatches; heterogeneity versus interoperability. In: AAAI Spring Symposium on Ontological Engineering, Stanford University, California, USA (1997)Google Scholar
  8. 8.
    Cranefield, S., Purvis, M.: UML as an ontology modelling language. In: IJCAI (1999)Google Scholar
  9. 9.
    Franch, X., Palomares, C., Quer, C., Renault, S., De Lazzer, F.: A metamodel for software requirement patterns. In: Wieringa, R., Persson, A. (eds.) REFSQ 2010. LNCS, vol. 6182, pp. 85–90. Springer, Heidelberg (2010). Scholar
  10. 10.
    Gómez, M., Adams, B., Maalej, W., Monperrus, M., Rouvoy, R.: App Store 2.0: from crowdsourced information to actionable feedback in mobile ecosystems. IEEE Softw. 34(2), 81–89 (2017)CrossRefGoogle Scholar
  11. 11.
    Kurtanović, Z., Maalej, M.: Mining User Rationale from Software Reviews. In: RE 2017 (2017)Google Scholar
  12. 12.
    Goh, C.H.: Representing and reasoning about semantic conflicts in heterogeneous information sources. Ph.D. thesis, MIT (1996)Google Scholar
  13. 13.
    IEEE Std 610.12-1990 - IEEE Standard Glossary of Software Engineering Terminology (1990)Google Scholar
  14. 14.
    Schenner, G., Bischof, S., Polleres, A., Steyskal, S.: Integrating distributed configurations with RDFS and SPARQL. In: confWS 2014 (2014)Google Scholar
  15. 15.
    Asikainen, T., Männistö, T., Soininen. T.: Kumbang: A Domain Ontology for Modelling Variability in Software Product Families. Adv. Eng. Inform. 21(1), 23–40 (2007)CrossRefGoogle Scholar
  16. 16.
    Wohlin, C., Runeson, P., Host, M., Ohlsson, M.C., Regnell, B., Wesslen, A.: Experimentation in Software Engineering: An Introduction. Kluwer Academic Publishers Norwell, MA, USA (2012)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Universitat Politècnica de Catalunya (UPC)BarcelonaSpain
  2. 2.Siemens AG ÖsterreichViennaAustria
  3. 3.Graz University of TechnologyGrazAustria
  4. 4.University of Hamburg/HITeCHamburgGermany
  5. 5.Vogella GmbHHamburgGermany
  6. 6.University of HelsinkiHelsinkiFinland

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