Evaluation of Semantic Web Ontologies for Modelling Art Collections

  • Danfeng Liu
  • Antonis BikakisEmail author
  • Andreas Vlachidis
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 767)


The need for organising, sharing and digitally processing Cultural Heritage (CH) information has led to the development of formal knowledge representation models (ontologies) for the CH domain. Based on RDF and OWL, the standard data model and ontology language of the Semantic Web, ontologies such as CIDOC-CRM, the Europeana Data Model and VRA, offer enhanced representation capabilities, but also support for inference, querying and interlinking through the Web. This paper presents the results of a small-scale evaluation of the three most commonly used CH ontologies, with respect to their capacity to fulfil the data modelling requirements of art collections.



This work was partially supported by CrossCult: “Empowering reuse of digital cultural heritage in context-aware crosscuts of European history”, funded by the European Union’s Horizon 2020 research and innovation program, Grant #693150.


  1. 1.
    Mantegari, G.: Cultural heritage on the semantic web: from representation to fruition. Ph.D. dissertation, Universita degli Studi di Milano Bicocca (2009).
  2. 2.
    Hyvönen, E.: Publishing and Using Cultural Heritage Linked Data on the Semantic Web. Morgan & Claypool, Palo Alto (2012)Google Scholar
  3. 3.
    Doerr, M.: The CIDOC conceptual reference module: an ontological approach to semantic interoperability of metadata. AI Mag. 24(3), 75–92 (2003)Google Scholar
  4. 4.
    Doerr, M., Meghini, C., Isaac, A., Hennicke, S., Gradmann, S.: The Europeana data model (EDM). In: World Library and Information Congress: 76th IFLA General Conference and Assembly, Gothenburg, Sweden, 10–15 August 2010Google Scholar
  5. 5.
    Europeana: EDM mapping guidelines V2.3 (2016).
  6. 6.
    The Library of Congress: VRA Core 4.0 schemas and documentation (2007).
  7. 7.
    Brank, J., Grobelnik, M., Mladenić, D.: A survey of ontology evaluation techniques. In: Proceedings of the Conference on Data Mining and Data Warehouses (SiKDD 2005) (2005)Google Scholar
  8. 8.
    Duque-Ramos, A., Fernández-Breis, J.T., Stevens, R., Aussenac-Gilles, N.: OQuaRE: a SQuaRE-based approach for evaluating the quality of ontologies. J. Res. Pract. Inf. Technol. 43(2), 41–58 (2011)Google Scholar
  9. 9.
    Hlomani, H., Stacey, D.: Approaches, methods, metrics, measures, and subjectivity in ontology evaluation: a survey. Semant. Web J. 1(5), 1–11 (2014)Google Scholar
  10. 10.
    Burton-Jones, A., Storey, V.C., Sugumaran, V., et al.: A semiotic metrics suite for assessing the quality of ontologies. Data Knowl. Eng. 55(1), 84–102 (2005)CrossRefGoogle Scholar
  11. 11.
    Vrandečić, D.: Ontology evaluation. Ph.D. thesis, Karlscruhe Institute of Technology, Karlscruhe, Germany (2010).
  12. 12.
    Vrandečić, D.: Ontology evaluation. In: Staab, S., Studer, R. (eds.) Handbook on ontologies, pp. 293–314. Springer, Heidelberg (2009). doi: 10.1007/978-3-540-92673-3_13 Google Scholar
  13. 13.
    Van Assem, M.: RDF/OWL representation of VRA (2005).

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Danfeng Liu
    • 1
  • Antonis Bikakis
    • 1
    Email author
  • Andreas Vlachidis
    • 1
  1. 1.Department of Information StudiesUniversity College LondonLondonUK

Personalised recommendations