User-Driven Ontology Population from Linked Data Sources

  • Panagiotis Mitzias
  • Marina Riga
  • Efstratios KontopoulosEmail author
  • Thanos G. Stavropoulos
  • Stelios Andreadis
  • Georgios Meditskos
  • Ioannis Kompatsiaris
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 649)


In order for ontology-based applications to be deployed in real-life scenarios, significant volumes of data are required to populate the underlying models. Populating ontologies manually is a time-consuming and error-prone task and, thus, research has shifted its attention to automatic ontology population methodologies. However, the majority of the proposed approaches and tools focus on analysing natural language text and often neglect other more appropriate sources of information, such as the already structured and semantically rich sets of Linked Data. The paper presents PROPheT, a novel ontology population tool for retrieving instances from Linked Data sources and subsequently inserting them into an OWL ontology. The tool, to the best of our knowledge, offers entirely novel ontology population functionality to a great extent and has already been positively received according to user evaluation.


Ontologies OWL Ontology population Linked data DBpedia 



This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 601138.


  1. 1.
    Stephan, G.S., Pascal, H.S., Andreas, A.S.: Knowledge representation and ontologies. In: Studer, R., Grimm, S., Abecker, A. (eds.) Semantic Web Services: Concepts, Technologies, and Applications, pp. 51–105. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Buitelaar, P., Cimiano, P.: Ontology Learning and Population: Bridging the Gap Between Text and Knowledge, vol. 167. Ios Press, Amsterdam (2008)zbMATHGoogle Scholar
  3. 3.
    Petasis, G., Karkaletsis, V., Paliouras, G., Krithara, A., Zavitsanos, E.: Ontology population and enrichment: state of the art. In: Paliouras, G., Spyropoulos, C.D., Tsatsaronis, G. (eds.) Multimedia Information Extraction. LNCS, vol. 6050, pp. 134–166. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Bizer, C., Heath, T., Idehen, K., Berners-Lee, T.: Linked data on the web (LDOW2008). In: Proceedings of 17th International Conference on World Wide Web, pp. 1265–1266. ACM, April 2008Google Scholar
  5. 5.
    Ghawi, R., Cullot, N.: Database-to-ontology mapping generation for semantic interoperability. In: VLDB 2007 Conference, VLDB Endowment, Vienna, Austria, pp. 1–8. ACM (2007)Google Scholar
  6. 6.
    Zhao, L., Ichise, R.: Mid-ontology learning from linked data. In: Pan, J.Z., Chen, H., Kim, H.-G., Li, J., Wu, Z., Horrocks, I., Mizoguchi, R., Wu, Z. (eds.) JIST 2011. LNCS, vol. 7185, pp. 112–127. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Gavankar, C., Kulkarni, A., Fang Li, Y., Ramakrishnan, G.: Enriching an academic knowledge base using linked open data. In: Proceedings of Workshop on Speech and Language Processing Tools in Education in 24th International Conference on Computational Linguistics, pp. 51–60 (2012)Google Scholar
  8. 8.
    Maynard, D., Funk, A., Peters, W.: SPRAT: a tool for automatic semantic pattern-based ontology population. In: International Conference for Digital Libraries and the Semantic Web, Trento, Italy (2009)Google Scholar
  9. 9.
    Velardi, P., Navigli, R., Missikoff, M.: Integrated approach for web ontology learning and engineering. IEEE Comput. 35(11), 60–63 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    Han, L., Finin, T.W., Parr, C.S., Sachs, J., Joshi, A.: RDF123: from spreadsheets to RDF. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 451–466. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Modica, G.A., Gal, A., Jamil, H.M.: The use of machine-generated ontologies in dynamic information seeking. In: Batini, C., Giunchiglia, F., Giorgini, P., Mecella, M. (eds.) CoopIS 2001. LNCS, vol. 2172, pp. 433–447. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Miles, A., Bechhofer, S.: SKOS simple knowledge organization system reference. In: W3C recommendation, 18, W3C (2009)Google Scholar
  13. 13.
    Brooke, J.: SUS-a quick and dirty usability scale. Usability Eval. Indus. 189(194), 4–7 (1996)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Panagiotis Mitzias
    • 1
  • Marina Riga
    • 1
  • Efstratios Kontopoulos
    • 1
    Email author
  • Thanos G. Stavropoulos
    • 1
  • Stelios Andreadis
    • 1
  • Georgios Meditskos
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Information Technologies InstituteThessalonikiGreece

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