Advertisement

Towards Vagueness-Oriented Quality Assessment of Ontologies

  • Panos Alexopoulos
  • Phivos Mylonas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8445)

Abstract

Ontology evaluation has been recognized for a long time now as an important part of the ontology development lifecycle, and several methods, processes and metrics have been developed for that purpose. Nevertheless, vagueness is a quality dimension that has been neglected from most current approaches. Vagueness is a common human knowledge and linguistic phenomenon, typically manifested by terms and concepts that lack clear applicability conditions and boundaries such as high, expert, bad, near etc. As such, the existence of vague terminology in an ontology may hamper the latter’s quality, primarily in terms of shareability and meaning explicitness. With that in mind, in this short paper we argue for the need of including vagueness in the ontology evaluation activity and propose a set of metrics to be used towards that goal.

Keywords

Domain Expert Semantic Data Fuzzy Ontology Vague Statement Ontology Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alexopoulos, P., Villazon-Terrazas, B., Pan, J.Z.: Towards vagueness-aware semantic data. In: URSW. CEUR Workshop Proceedings, vol. 1073, pp. 40–45. CEUR-WS.org (2013)Google Scholar
  2. 2.
    Alexopoulos, P., Wallace, M., Kafentzis, K., Thomopoulos, A.: A fuzzy knowledge-based decision support system for tender call evaluation. In: Iliadis, Maglogiann, Tsoumakasis, Vlahavas, Bramer (eds.) AIAI. IFIP, vol. 296, pp. 51–59. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    Bobillo, F., Straccia, U.: Fuzzy ontology representation using owl 2. International Journal of Approximate Reasoning 52(7), 1073–1094 (2011)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Brank, J., Madenic, D., Groblenik, M.: Gold standard based ontology evaluation using instance assignment. In: Proceedings of the 4th Workshop on Evaluating Ontologies for the Web (EON 2006), Edinburgh, Scotland (May 2006)Google Scholar
  5. 5.
    Brewster, C., Alani, H., Dasmahapatra, S., Wilks, Y.: Data-driven ontology evaluation. In: Proceedings of the Language Resources and Evaluation Conference (LREC 2004), pp. 164–168. European Language Resources Association, Lisbon (2004)Google Scholar
  6. 6.
    Chandrasekaran, B., Josephson, J., Benjamins, R.: What are ontologies and why do we need them? IEEE Intelligent Systems 14(1), 20–26 (1999)CrossRefGoogle Scholar
  7. 7.
    Ciancarini, P., Iorio, A.D., Nuzzolese, A.G., Peroni, S., Vitali, F.: Characterising citations in scholarly articles: An experiment. In: AIC@AI*IA. CEUR Workshop Proceedings, vol. 1100, pp. 124–129. CEUR-WS.org (2013)Google Scholar
  8. 8.
    Hyde, D.: Vagueness, Logic and Ontology. Ashgate New Critical Thinking in Philosophy (2008)Google Scholar
  9. 9.
    Porzel, R., Malaka, R.: A task-based approach for ontology evaluation. In: Proceedings of ECAI 2004 Workshop on Ontology Learning and Population, Valencia, Spain (August 2004)Google Scholar
  10. 10.
    Sim, J., Wright, C.C.: The kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Physical Therapy (March 2005)Google Scholar
  11. 11.
    Tartir, S., Arpinar, I.B., Moore, M., Sheth, A.P., Aleman-Meza, B.: OntoQA: Metric-based ontology quality analysis. In: Proceedings of IEEE Workshop on Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Panos Alexopoulos
    • 1
  • Phivos Mylonas
    • 2
  1. 1.iSOCOMadridSpain
  2. 2.Department of InformaticsIonian UniversityCorfuGreece

Personalised recommendations