A Quality Assurance Workflow for Ontologies Based on Semantic Regularities

  • Eleni Mikroyannidi
  • Manuel Quesada-Martínez
  • Dmitry Tsarkov
  • Jesualdo Tomás Fernández Breis
  • Robert Stevens
  • Ignazio Palmisano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8876)

Abstract

Syntactic regularities or syntactic patterns are sets of axioms in an OWL ontology with a regular structure. Detecting these patterns and reporting them in human readable form should help the understanding the authoring style of an ontology and is therefore useful in itself. However, pattern detection is sensitive to syntactic variations in the assertions; axioms that are semantically equivalent but syntactically different can reduce the effectiveness of the technique. Semantic regularity analysis focuses on the knowledge encoded in the ontology, rather than how it is spelled out, which is the focus of syntactic regularity analysis. Cluster analysis of the information provided by an OWL DL reasoner mitigates this sensitivity, providing measurable benefits over purely syntactic patterns - an example being patterns that are instantiated only in the entailments of an ontology. In this paper, we demonstrate, using SNOMED-CT, how the detection of semantic regularities in entailed axioms can be used in ontology quality assurance, in combination with lexical techniques. We also show how the detection of irregularities, i.e., deviations from a pattern, are useful for the same purpose. We evaluate and discuss the results of performing a semantic pattern inspection and we compare them against existing work on syntactic regularity detection. Systematic extraction of lexical, syntactic and semantic patterns is used and a quality assurance workflow that combines these patterns is presented.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blomqvist, E., Sandkuhl, K.: Patterns in ontology engineering: Classification of ontology patterns. In: ICEIS (3), pp. 413–416 (2005)Google Scholar
  2. 2.
    Clark, P.: Knowledge patterns. Knowledge Engineering: Practice and Patterns, pp. 1–3 (2008)Google Scholar
  3. 3.
    Davis, R.: Interactive transfer of expertise: Acquisition of new inference rules. Artificial Intelligence 12(2), 121–157 (1979)CrossRefGoogle Scholar
  4. 4.
    European Commission. Semantic interoperability for better health and safer healthcare. deployment and research roadmap for Europe (2009) ISBN-13: 978-92-79-11139-6Google Scholar
  5. 5.
    Glimm, B., Horrocks, I., Motik, B.: Optimized Description Logic Reasoning via Core Blocking. In: Giesl, J., Hähnle, R. (eds.) IJCAR 2010. LNCS, vol. 6173, pp. 457–471. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Horrocks, I., Kutz, O., Sattler, U.: The even more irresistible SROIQ. In: Principles of Knowledge Representation and Reasoning, pp. 57–67 (2006)Google Scholar
  7. 7.
    Józefowska, J., Ławrynowicz, A., Łukaszewski, T.: Towards discovery of frequent patterns in description logics with rules. Rules and Rule Markup Languages for the Semantic Web, 84–97 (2005)Google Scholar
  8. 8.
    Khan, M.T., Blomqvist, E.: Ontology design pattern detection-initial method and usage scenarios. In: SEMAPRO 2010, The Fourth International Conference on Advances in Semantic Processing, pp. 19–24 (2010)Google Scholar
  9. 9.
    Mikroyannidi, E., Iannone, L., Stevens, R., Rector, A.: Inspecting regularities in ontology design using clustering. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 438–453. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Mikroyannidi, E., Stevens, R., Iannone, L., Rector, A.: Analysing Syntactic Regularities and Irregularities in SNOMED-CT. Journal of biomedical semantics 3(1), 8 (2012)CrossRefGoogle Scholar
  11. 11.
    Motik, B., Patel-Schneider, P., Parsia, B., Bock, C., Fokoue, A., Haase, P., Hoekstra, R., Horrocks, I., Ruttenberg, A., Sattler, U., et al.: OWL 2 Web Ontology Language: Structural Specification and Functional-Style Syntax. In: W3C Recommendation, 2nd edn., vol. 11 (2012)Google Scholar
  12. 12.
    Quesada-Martínez, M., Fernández-Breis, J.T., Stevens, R.: Enrichment of OWL ontologies: a method for defining axioms from labels. In: Moss, L., Sleeman, D. (eds.) Proceedings of the International Workshop on Capturing and Refining Knowledge in the Medical Domain (KMED 2012), Galway, Ireland, pp. 5–10 (2012)Google Scholar
  13. 13.
    Quesada-Martínez, M., Fernández-Breis, J.T., Stevens, R.: Lexical characterization and analysis of the bioPortal ontologies. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS, vol. 7885, pp. 206–215. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Rector, A., Iannone, L.: Lexically suggest, logically define: Quality assurance of the use of qualifiers and expected results of post-coordination in SNOMED CT. Journal of Biomedical Informatics 45(2), 199 (2012)CrossRefGoogle Scholar
  15. 15.
    Rector, A.L., Brandt, S., Schneider, T.: Getting the foot out of the pelvis: modeling problems affecting use of SNOMED CT hierarchies in practical applications. Journal of the American Medical Informatics Association 18(4), 432–440 (2011)CrossRefGoogle Scholar
  16. 16.
    Rosse, C., Mejino, J., et al.: A reference ontology for biomedical informatics: the Foundational Model of Anatomy. Journal of biomedical informatics 36(6), 478–500 (2003)CrossRefGoogle Scholar
  17. 17.
    Rosse, C., Mejino Jr., J.L.: The foundational model of anatomy ontology. In: Anatomy Ontologies for Bioinformatics, pp. 59–117. Springer (2008)Google Scholar
  18. 18.
    Spackman, K., Dionne, R., Mays, E., Weis, J.: Role grouping as an extension to the description logic of ontylog, motivated by concept modeling in snomed. In: Proceedings of the AMIA Symposium, p. 712. American Medical Informatics Association (2002)Google Scholar
  19. 19.
    Spackman, K.A., Campbell, K.E., Côté, R.A.: SNOMED RT: a reference terminology for health care. In: Proceedings of the AMIA Annual Fall Symposium, p. 640. American Medical Informatics Association (1997)Google Scholar
  20. 20.
    Šváb-Zamazal, O., Scharffe, F., Svátek, V.: Preliminary results of logical ontology pattern detection using sparql and lexical heuristics. In: Proceedings of the Workshop on Ontology Patterns (WOP-2009) (2009)Google Scholar
  21. 21.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley (2005)Google Scholar
  22. 22.
    Third, A.: Hidden semantics: what can we learn from the names in an ontology? In: Proceedings of the Seventh International Natural Language Generation Conference, Utica, IL, USA (May 2012)Google Scholar
  23. 23.
    Tsarkov, D., Horrocks, I.: faCT++ description logic reasoner: System description. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 292–297. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  24. 24.
    Wang, Y., Halper, M., Min, H., Perl, Y., Chen, Y., Spackman, K.: Structural methodologies for auditing snomed. Journal of Biomedical Informatics 40(5), 561–581 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Eleni Mikroyannidi
    • 1
  • Manuel Quesada-Martínez
    • 2
  • Dmitry Tsarkov
    • 1
  • Jesualdo Tomás Fernández Breis
    • 2
  • Robert Stevens
    • 1
  • Ignazio Palmisano
    • 1
  1. 1.University of ManchesterManchesterUK
  2. 2.Universidad de Murcia, IMIB-ArrixacaMurciaSpain

Personalised recommendations