Navigating OWL 2 Ontologies Through Graph Projection

  • Ahmet SoyluEmail author
  • Evgeny Kharlamov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 846)


Ontologies are powerful, yet often complex, assets for representing, exchanging, and reasoning over data. Particularly, OWL 2 ontologies have been key for constructing semantic knowledge graphs. Ability to navigate ontologies is essential for supporting various knowledge engineering tasks such as querying and domain exploration. To this end, in this short paper, we describe an approach for projecting the non-hierarchical topology of an OWL 2 ontology into a graph. The approach has been implemented in two tools, one for visual query formulation and one for faceted search, and evaluated under different use cases.


OWL 2 Ontologies Graph navigation Knowledge graphs 


  1. 1.
    Arenas, M., et al.: Faceted search over RDF-based knowledge graphs. J. Web Semant. 37–38, 55–74 (2016)CrossRefGoogle Scholar
  2. 2.
    Baader, F., et al. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  3. 3.
    Biega, J., et al.: Inside YAGO2s: a transparent information extraction architecture. In: Proceedings of the 22nd International Conference on World Wide Web (WWW 2013), pp. 325–328. ACM, New York (2013)Google Scholar
  4. 4.
    Grau, B.C., et al.: OWL 2: the next step for OWL. J. Web Semant. 6(4), 309–322 (2008)CrossRefGoogle Scholar
  5. 5.
    Cuenca Grau, B., et al.: Towards query formulation, query-driven ontology extensions in OBDA systems. In: Proceedings of the 10th International Workshop on OWL: Experiences and Directions (OWLED 2013) (2013)Google Scholar
  6. 6.
    Katifori, A., et al.: Ontology visualization methods - a survey. ACM Comput. Surv. 39(4), 10 (2007)CrossRefGoogle Scholar
  7. 7.
    Kharlamov, E., et al.: Ontology based data access in statoil. J. Web Seman. 44, 3–36 (2017)CrossRefGoogle Scholar
  8. 8.
    Kharlamov, E., et al.: Semantic access to streaming and static data at Siemens. J. Web Seman. 44, 54–74 (2017)CrossRefGoogle Scholar
  9. 9.
    Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Seman. Web 6(2), 167–195 (2015)Google Scholar
  10. 10.
    Lohmann, S., et al.: Visualizing ontologies with VOWL. Seman. Web 7(4), 399–419 (2016)CrossRefGoogle Scholar
  11. 11.
    Soylu, A., et al.: OptiqueVQS: a visual query system over ontologies for industry. Semantic Web 9(5), 627–660 (2018)CrossRefGoogle Scholar
  12. 12.
    Soylu, A., et al.: Ubiquitous web navigation through harvesting embedded semantic data: a mobile scenario. Integr. Comput.-Aided Eng. 19(1), 93–109 (2012)CrossRefGoogle Scholar
  13. 13.
    Soylu, A., et al.: Experiencing optiqueVQS: a multi-paradigm and ontology-based visual query system for end users. Univ. Access Inf. Soc. 15(1), 129–152 (2016)CrossRefGoogle Scholar
  14. 14.
    Soylu, A., et al.: Ontology-based end-user visual query formulation: why, what, who, how, and which? Univ. Access Inf. Soc. 16(2), 435–467 (2017)CrossRefGoogle Scholar
  15. 15.
    Soylu, A., et al.: Querying industrial stream-temporal data: an ontology-based visual approach. J. Ambient Intell. Smart Environ. 9(1), 77–95 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Suchanek, F.M., et al.: Knowledge bases in the age of big data analytics. Proc. VLDB Endowment 7(13), 1713–1714 (2014)CrossRefGoogle Scholar
  17. 17.
    Yan, J., et al.: A retrospective of knowledge graphs. Front. Comput. Sci. 12(1), 55–74 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Norwegian University of Science and TechnologyGjøvikNorway
  2. 2.SINTEF DigitalOsloNorway
  3. 3.University of OxfordOxfordUK

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