Skip to main content
SpringerLink
Log in

IKARUS-Onto: a methodology to develop fuzzy ontologies from crisp ones

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Fuzzy Ontologies comprise a relatively new knowledge representation paradigm that is being increasingly applied in application scenarios in which the treatment and utilization of vague or imprecise knowledge are important. However, the majority of research in the area has mostly focused on the development of conceptual formalisms for representing (and reasoning with) fuzzy ontologies, while the methodological issues entailed within the development process of such an ontology have been so far neglected. With that in mind, we present in this paper IKARUS-Onto, a comprehensive methodology for developing fuzzy ontologies from existing crisp ones that significantly enhances the effectiveness of the fuzzy ontology development process and the quality, in terms of accuracy, shareability and reusability, of the process’s output.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Agarwal S, Lamparter S (2005) A semantic matchmaking portal for electronic markets. In: Proceedings of the seventh IEEE international conference on E-commerce technology, 19–22 July 2005

  2. Alexopoulos P, Wallace M, Kafentzis K, Thomopoulos (2009) A fuzzy knowledge-based decision support system for tender call evaluation. In: Proceedings of the 5th IFIP conference on artificial intelligence applications and innovations (AIAI 2009)

  3. Alexopoulos P, Wallace M, Kafentzis K, Askounis D (2010) Utilizing imprecise knowledge in ontology-based CBR systems through fuzzy algebra. Int J Fuzzy Syst 12(1): 1–14

    Google Scholar 

  4. Baeza-Yates R, Tiberi A (2007) Extracting semantic relations from query logs. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’07). ACM, New York, pp 76–85

  5. Bechhofer S, van Harmelen F, Hendler J, Horrocks I, McGuinness D, Patel Schneider P, Stein LA (2004) OWL web ontology language reference. W3C recommendation, 10 Feb 2004

  6. Bobillo F, Delgado M, Gomez-Romero J (2008) DeLorean: a reasoner for fuzzy OWL 1.1. In: Proceedings of the 4th international workshop on uncertainty reasoning for the semantic web (URSW 2008). CEUR workshop proceedings 423. Karlsruhe, Oct 2008

  7. Bobillo F, Straccia U (2008) fuzzyDL: an expressive fuzzy description logic reasoner. In: Proceedings of the 2008 international conference on fuzzy systems

  8. Bouamrane M-M, Rector A, Hurrell M (2010) Using OWL ontologies for adaptive patient information modeling and preoperative clinical decision support. Knowledge and information systems. Springer, London, p 114

  9. Calegari S, Sanchez E (2007) A fuzzy ontology-approach to improve semantic information retrieval. In: Bobillo F, da Costa PCG, D’Amato C, Fanizzi N, Fung F, Lukasiewicz T, Martin T, Nickles M, Peng Y, Pool M, Smrz P, Vojtas P (eds) Proceedings of the third ISWC workshop on uncertainty reasoning for the semantic web—URSW’07, vol 327

  10. Cimiano P (2006) Ontology learning and population from text: algorithms, evaluation and applications. Springer-Verlag, New York, Inc., Secaucus

    Google Scholar 

  11. Chandrasekaran B, Josephson JR, Benjamins VR (1999) What are ontologies, and why do we need them. IEEE Intell Syst 14(1): 20–26

    Article  Google Scholar 

  12. Chen W, Yang Q, Zhu L, Wen B (2009) Research on automatic fuzzy ontology generation from fuzzy context. In: Proceedings of the 2009 second international conference on intelligent computation technology and automation—volume 02 (ICICTA ’09), vol 2. IEEE Computer Society, Washington, pp 764–767

  13. Chen CL, Tseng F, Liang T (2010) An integration of fuzzy association rules and WordNet for document clustering. Knowledge and information systems. Springer, London, p 122

  14. Escovar ELG, Yaguinima CA, Biajic M (2006) Using fuzzy ontologies to extend semantically similar data mining. In: XXI Simposio Brasileiro de Banco de Dados (SBBD), Florianopolis, p 16–30

  15. Fernandez Lopez M, Gomez Perez A, Juristo N (1997) Methontology: from ontological art towards ontological engineering. In: Spring symposium on ontological engineering of AAAI, pp 33–40

  16. Fodeh S, Punch B, Tan, P-N (2011) On ontology-driven document clustering using core semantic features. Knowledge and information systems. Springer, London, p 127

  17. Gomez-Perez A, Corcho O, Fernandez-Lopez M (2004) Ontological engineering. Springer-Verlag London Limited

  18. Gruninger M, Fox MS (1995) Methodology for the design and evaluation of ontologies. In: IJCAI95 on workshop basic ontological issues in knowledge sharing

  19. Jarrar M, Meersman R (2008) Ontology engineering—the DOGMA approach. In: Advances in web semantics, vol I, LNCS 4891. Springer, Berlin

  20. Jurisica I, Mylopoulos J, Yu E (2004) Ontologies for knowledge management: an information systems perspective. Knowledge and information systems, vol 6, no. 4. Springer, London, pp 380–401

  21. Hyde D (2008) Vagueness, logic and ontology. Ashgate new critical thinking in philosophy

  22. Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic, theory and applications. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  23. Kotis K, Vouros G (2006) Human-centered ontology engineering: the HCOME methodology. Knowledge and information systems (KAIS), vol 10. Springer, London, pp 109–131

  24. Lau RY, Song D, Li Y, Cheung TCH, Hao JX (2009) Toward a fuzzy domain ontology extraction method for adaptive e-learning. IEEE Trans Knowl Data Eng 21(6): 800–813

    Article  Google Scholar 

  25. Lee CS, Jian ZW, Huang LK (2005) A fuzzy ontology and its application to news summarization. IEEE Trans Syst Man Cybern B 35(5): 859–880

    Article  Google Scholar 

  26. Liu K, Fang B, Zhang W (2010) Ontology emergence from folksonomies. In: Proceedings of the 19th ACM international conference on information and knowledge management (CIKM ’10). ACM, New York

  27. Mailis T, Stoilos G, Stamou G (2010) Expressive reasoning with horn rules and fuzzy description logics. Knowledge and information systems, vol 25, no. 1. Springer, London, pp 105–136

  28. McGee V, McLaughlin B (1994) Distinctions without a difference. South J Philos 33(suppl): 203–251

    Google Scholar 

  29. Parry D (2004) A fuzzy ontology for medical document retrieval. In: Hogan J, Montague P, Purvis M, Steketee C (eds) Proceedings of the second workshop on Australasian information security, data mining and web intelligence, and software internationalisation—vol 32. Dunedin. ACM international conference proceeding series, vol 54. Australian Computer Society, Darlinghurst, pp 121–126

  30. Pinto S, Martins J (2004) Ontologies: how can they be built?. Knowl Inf Syst 6: 441–464

    Article  Google Scholar 

  31. Ramezani M, Witschel HF, Braun S, Zacharias V (2010) Using machine learning to support continuous ontology development. In: Cimiano P, Sofia Pinto H (eds) Proceedings of the 17th international conference on Knowledge engineering and management by the masses (EKAW’10). Springer, Berlin, pp 381–390

  32. Reichartz F, Korte H, Paass G (2010) Semantic relation extraction with kernels over typed dependency trees. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’10). ACM, New York, pp 773–782

  33. Sanchez D, Isern D, Millan M (2010) Content annotation for the semantic web: an automatic web-based approach. Knowledge and information systems. Springer, London, p 126

  34. Shapiro S (2006) Vagueness in context. Oxford University Press, USA

    Book  Google Scholar 

  35. Simou N, Kollias S (2007) FiRE: a fuzzy reasoning engine for impecise knowledge. K-space PhD students workshop. Berlin, 14 Sept 2007

  36. Stoilos G, Straccia U, Stamou G, Pan JZ (2006) General concept inclusions in fuzzy description logics. In: 17th European conference on artificial intelligence (ECAI 06). Riva del Garda

  37. Stoilos G, Stamou G, Pan JZ, Simou N, Tzouvaras V (2008) Reasoning with the fuzzy description logic f-SHIN: theory, practice and applications. In: Costa PCG, d’Amato C et al (eds) Uncertainty reasoning for the semantic web I

  38. Szumlanski S, Gomez F (2010) Automatically acquiring a semantic network of related concepts. In: Proceedings of the 19th ACM international conference on information and knowledge management (CIKM-10). Toronto, pp 19–28

  39. Tho QT, Hui SC, Fong ACM, Cao TH (2006) Automatic fuzzy ontology generation for semantic web. IEEE Trans Knowl Data Eng 18(6): 842–856

    Article  Google Scholar 

  40. Thomas C, Sheth A (2006) On the expressiveness of the languages for the semantic web—making a case for ‘A little more’. In: Sanchez E (ed) Fuzzy logic and the semantic web. Elsevier, Amsterdam

    Google Scholar 

  41. Uschold M, King M (1995) Towards a methodology for building ontologies. In: IJCAI95 workshop on basic ontological issues in knowledge sharing, pp 6.1–6.10

  42. Vrandecic D, Pinto HS, Sure Y, Tempich C (2005) The DILIGENT knowledge processes. J Knowl Manag 9(5): 85–96

    Article  Google Scholar 

  43. Wallace M, Mylonas P, Akrivas G, Avrithis Y, Kollias S (2006) Automatic thematic categorization of multimedia documents using ontological information and fuzzy algebra. In: Ma Z (ed) Studies in fuzziness and soft computing, soft computing in ontologies and semantic web, vol 204. Springer, Berlin

  44. Zadeh LA (2003) From search engines to question-answering systems the need for new tools. In: The 12th IEEE international conference on fuzzy systems 2003, vol 2, pp 1107–1109

  45. Zhai J, Liang Y, Jiang J, Yu Y (2008) Fuzzy ontology models based on fuzzy linguistic variable for knowledge management and information retrieval. In: Intelligent information processing IV, pp 58–67

  46. Zhang F, Ma ZM, Fan G, Wang X (2010) Automatic fuzzy semantic web ontology learning from fuzzy object-oriented database model. In: Bringas PG, Hameurlain A, Quirchmayr G (eds) Proceedings of the 21st international conference on database and expert systems applications: part I (DEXA’10). Springer, Berlin, pp 16–30

  47. Zhang F, Ma ZM, Cheng J, Meng X (2009) Fuzzy semantic web ontology learning from fuzzy UML model. In: Proceeding of the 18th ACM conference on Information and knowledge management (CIKM ’09). ACM, New York

  48. Zhou L (2007) Ontology learning: state of the art and open issues. Inf Technol Manag 8(3): 241–252

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IMC Technologies, Athens, Greece

    Panos Alexopoulos & Konstantinos Kafentzis

  2. Department of Computer Science and Technology, University of Peloponnese, Tripolis, Greece

    Manolis Wallace

  3. School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Panos Alexopoulos & Dimitris Askounis

Authors
  1. Panos Alexopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manolis Wallace
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Konstantinos Kafentzis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dimitris Askounis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Panos Alexopoulos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alexopoulos, P., Wallace, M., Kafentzis, K. et al. IKARUS-Onto: a methodology to develop fuzzy ontologies from crisp ones. Knowl Inf Syst 32, 667–695 (2012). https://doi.org/10.1007/s10115-011-0457-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-011-0457-6

Keywords

Search

Navigation