skip to main content
10.1145/3404709.3404771acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicfetConference Proceedingsconference-collections
research-article

Cooperative Domain Ontology Reduction Based on Power Sets

Authors Info & Claims
Published:13 July 2020Publication History

ABSTRACT

Ontology is widely used in the areas of knowledge engineering, web-based data mining, and others. The process of developing and evolving inter-organizational domain ontologies is easy to get much redundant information. PowerSets can be used to reduce the attributes of ontologies. In this paper, "Rule Finding Uniqueness," RFU is proposed for learning a set of rules in order to refine an ontology. The algorithm's primary goal is to generate unique rules that not only cover the initial set but also enhance reasoning. The claimed technique compresses Ontologies after it is already built or during the evolving process of the inter-organizational cooperative domain ontology. The proposed method can also be used to strengthen automatic and semi-automatic operations to develop and evolve ontologies. We can consider this approach as a maintenance operation that could be done periodically based on the ontology evolution frequency rate.

References

  1. J. Ashraf, E. Chang, O. K. Hussain, and F. K. Hussain, "Ontology usage analysis in the ontology lifecycle: A state-of-the-art review," Knowledge-Based Systems, vol. 80. pp. 34--47, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. F. Zablith et al., Ontology Evolution: A Process Centric Survey, vol. 00. 2013.Google ScholarGoogle Scholar
  3. R. Djedidi and M.-A. Aufaure, "Ontology Evolution: State of the Art and Future Directions," Ontol. Theory, Manag. Des. Adv. Tools Model., vol. 7, pp. 179--207, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. A. and K. E. Wa'el Mohsen, "The Scrum Framework for Cooperative Ontology Evolution," 2017.Google ScholarGoogle Scholar
  5. Z. Pawlak and A. Skowron, "Rudiments of rough sets," Inf. Sci. (Ny)., vol. 177, no. 1, pp. 3--27, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  6. Q. Liu, L. Chen, J. Zhang, and F. Min, "Knowledge Reduction in Inconsistent Decision Tables," in ADMA 2006. Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science, vol 4093, Berlin, Heidelberg: Springer, Berlin, Heidelberg, 2006, pp. 626--635.Google ScholarGoogle Scholar
  7. K. Schwaber, "Nexus Guide - The Definitive Guide to scaling Scrum with Nexus: The Rules of the Game," Scrum.org, 2018.Google ScholarGoogle Scholar
  8. H. Mirkil and P. R. Halmos, "Naive Set Theory.," Am. Math. Mon., 1961.Google ScholarGoogle Scholar
  9. M. Stenbeck, R. K. Hambleton, H. Swaminathan, and H. J. Rogers, "Fundamentals of Item Response Theory.," Contemp. Sociol, 1992.Google ScholarGoogle Scholar
  10. A. Kanamori, "Set Theory from Cantor to Cohen," in Philosophy of Mathematics, 2009.Google ScholarGoogle Scholar
  11. J. Issa, "Set Theory," Aγαη, 2019. [Online]. Available: https://www.encyclopedia.com/science-and-technology/mathematics/mathematics/set-theory.Google ScholarGoogle Scholar
  12. P. Kruszyński and K. Napiórkowski, "On the independence of local algebras II," Reports Math. Phys., vol. 4, no. 4, pp. 303--306, 1973.Google ScholarGoogle ScholarCross RefCross Ref
  13. G. Troullinou et al., "Ontology understanding without tears: The-summarization approach," Semant. Web, 2017.Google ScholarGoogle Scholar
  14. D. Ślęzak, "Searching for dynamic reducts in inconsistent decision tables," in Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'98), 1998, p. Volume: 2.Google ScholarGoogle Scholar
  15. P.-C. WANG, "Dynamic Reducts Generation Using Cascading Hashes" Int. J. Found. Comput. Sci., vol. 25, no. 02, pp. 219--246,2014.Google ScholarGoogle ScholarCross RefCross Ref
  16. D. Q. Miao, Y. Zhao, Y. Y. Yao, H. X. Li, and F. F. Xu, "Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model," Inf. Sci. (Ny)., vol. 179, no. 24, pp. 4140--4150, 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. M. Kryszkiewicz, "Comparative study of alternative types of knowledge reduction in inconsistent systems," Int. J. Intell. Syst., vol. 16, no. 1, pp. 105--120, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  18. A. Hunter and S. Konieczny, "Approaches to Measuring Inconsistent Information," Inconsistency Toler., vol. LNCS 3300, pp. 191--236, 2010.Google ScholarGoogle Scholar
  19. L. J. Halbeisen, "Axioms of set theory," in Springer Monographs in Mathematics, 2017.Google ScholarGoogle Scholar
  20. Zermelo-Fraenkel, "Zermelo-Fraenkel set theory," 2019. [Online]. Available: https://en.wikipedia.org/wiki/Zermelo-Fraenkel_set_theory.Google ScholarGoogle Scholar
  21. O. Verhodubs, "Ontology as a Source for Rule Generation," ArXiv, Apr. 2014.Google ScholarGoogle Scholar
  22. "UC Irvine Machine Learning Repository," 2019. [Online]. Available: http://archive.ics.uci.edu/ml/index.php.Google ScholarGoogle Scholar
  23. AberOWL, "AberOWL ontology repository and semantic search engine," 2019. [Online]. Available: http://aber-owl.net.Google ScholarGoogle Scholar
  24. The University Of Manchester, "Protege Matrix," 2019. [Online]. Available: https://protegewiki.stanford.edu/wiki/Matrix.Google ScholarGoogle Scholar
  25. C. Ochs, J. Geller, Y. Perl, and M. A. Musen, "A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies," J. Biomed. Inform., 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. The University Of Manchester, "Protege Matrix," 2019.Google ScholarGoogle Scholar
  27. M. Horridge and S. Bechhofer, "The OWL API: A Java API for OWL ontologies," Semant. Web, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  28. A. Krammer, B. Heinrich, M. Henneberger, and F. Lautenbacher, "Granularity of Services," Bus. Inf. Syst. Eng., 2011.Google ScholarGoogle Scholar
  29. D. Shadija, M. Rezai, and R. Hill, "Microservices: Granularity vs. Performance," in UCC 2017 Companion - Companion Proceedings of the 10th International Conference on Utility and Cloud Computing, 2017.Google ScholarGoogle Scholar
  30. Wikipedia, "Application lifecycle management," Wikipedia, 2017. [Online]. Available: https://en.wikipedia.org/wiki/Application_lifecycle_management.Google ScholarGoogle Scholar
  31. SMARTBEAR, "SoapUI | The Leading Open Source API Testing Tool," 2015. [Online]. Available: https://www.soapui.org/open-source.html.Google ScholarGoogle Scholar
  32. SMARTBEAR, "SoapUI | The Leading Open Source API Testing Tool," 2015.Google ScholarGoogle Scholar
  33. W. Mohsen, M. Aref, and K. ElBahnasy, "Software metrics for cooperative scrum based ontology analysis," in 2017 2nd International Conference on Knowledge Engineering and Applications, ICKEA 2017, 2017, vol. 2017-Janua, pp. 60--70.Google ScholarGoogle Scholar
  34. N. Guarino and C. A. Welty, "An Overview of OntoClean," in Handbook on Ontologies, 2009.Google ScholarGoogle Scholar
  35. S. Tartir and I. B. Arpinar, "Ontology evaluation and ranking using OntoQA," in ICSC 2007 International Conference on Semantic Computing, 2007.Google ScholarGoogle Scholar
  36. A. Lozano-Tello and A. Gómez-Pérez, "ONTOMETRIC: A Method to Choose the Appropriate Ontology," J. Database Manag., 2004.Google ScholarGoogle ScholarCross RefCross Ref
  37. J. García, F. J. García-Peñalvo, and R. Therón, "A survey on ontology metrics," in Communications in Computer and Information Science, 2010.Google ScholarGoogle Scholar

Index Terms

  1. Cooperative Domain Ontology Reduction Based on Power Sets

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICFET '20: Proceedings of the 6th International Conference on Frontiers of Educational Technologies
      June 2020
      235 pages
      ISBN:9781450375337
      DOI:10.1145/3404709

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 July 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader