Nothing Special   »   [go: up one dir, main page]

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

Cooperative Domain Ontology Reduction Based on Power Sets

Published: 13 July 2020 Publication 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.
[2]
F. Zablith et al., Ontology Evolution: A Process Centric Survey, vol. 00. 2013.
[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.
[4]
M. A. and K. E. Wa'el Mohsen, "The Scrum Framework for Cooperative Ontology Evolution," 2017.
[5]
Z. Pawlak and A. Skowron, "Rudiments of rough sets," Inf. Sci. (Ny)., vol. 177, no. 1, pp. 3--27, 2007.
[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.
[7]
K. Schwaber, "Nexus Guide - The Definitive Guide to scaling Scrum with Nexus: The Rules of the Game," Scrum.org, 2018.
[8]
H. Mirkil and P. R. Halmos, "Naive Set Theory.," Am. Math. Mon., 1961.
[9]
M. Stenbeck, R. K. Hambleton, H. Swaminathan, and H. J. Rogers, "Fundamentals of Item Response Theory.," Contemp. Sociol, 1992.
[10]
A. Kanamori, "Set Theory from Cantor to Cohen," in Philosophy of Mathematics, 2009.
[11]
J. Issa, "Set Theory," Aγαη, 2019. [Online]. Available: https://www.encyclopedia.com/science-and-technology/mathematics/mathematics/set-theory.
[12]
P. Kruszyński and K. Napiórkowski, "On the independence of local algebras II," Reports Math. Phys., vol. 4, no. 4, pp. 303--306, 1973.
[13]
G. Troullinou et al., "Ontology understanding without tears: The-summarization approach," Semant. Web, 2017.
[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.
[15]
P.-C. WANG, "Dynamic Reducts Generation Using Cascading Hashes" Int. J. Found. Comput. Sci., vol. 25, no. 02, pp. 219--246,2014.
[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.
[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.
[18]
A. Hunter and S. Konieczny, "Approaches to Measuring Inconsistent Information," Inconsistency Toler., vol. LNCS 3300, pp. 191--236, 2010.
[19]
L. J. Halbeisen, "Axioms of set theory," in Springer Monographs in Mathematics, 2017.
[20]
Zermelo-Fraenkel, "Zermelo-Fraenkel set theory," 2019. [Online]. Available: https://en.wikipedia.org/wiki/Zermelo-Fraenkel_set_theory.
[21]
O. Verhodubs, "Ontology as a Source for Rule Generation," ArXiv, Apr. 2014.
[22]
"UC Irvine Machine Learning Repository," 2019. [Online]. Available: http://archive.ics.uci.edu/ml/index.php.
[23]
AberOWL, "AberOWL ontology repository and semantic search engine," 2019. [Online]. Available: http://aber-owl.net.
[24]
The University Of Manchester, "Protege Matrix," 2019. [Online]. Available: https://protegewiki.stanford.edu/wiki/Matrix.
[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.
[26]
The University Of Manchester, "Protege Matrix," 2019.
[27]
M. Horridge and S. Bechhofer, "The OWL API: A Java API for OWL ontologies," Semant. Web, 2011.
[28]
A. Krammer, B. Heinrich, M. Henneberger, and F. Lautenbacher, "Granularity of Services," Bus. Inf. Syst. Eng., 2011.
[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.
[30]
Wikipedia, "Application lifecycle management," Wikipedia, 2017. [Online]. Available: https://en.wikipedia.org/wiki/Application_lifecycle_management.
[31]
SMARTBEAR, "SoapUI | The Leading Open Source API Testing Tool," 2015. [Online]. Available: https://www.soapui.org/open-source.html.
[32]
SMARTBEAR, "SoapUI | The Leading Open Source API Testing Tool," 2015.
[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.
[34]
N. Guarino and C. A. Welty, "An Overview of OntoClean," in Handbook on Ontologies, 2009.
[35]
S. Tartir and I. B. Arpinar, "Ontology evaluation and ranking using OntoQA," in ICSC 2007 International Conference on Semantic Computing, 2007.
[36]
A. Lozano-Tello and A. Gómez-Pérez, "ONTOMETRIC: A Method to Choose the Appropriate Ontology," J. Database Manag., 2004.
[37]
J. García, F. J. García-Peñalvo, and R. Therón, "A survey on ontology metrics," in Communications in Computer and Information Science, 2010.

Cited By

View all
  • (2021)Semantic data mining in the information ageInternational Journal of Intelligent Systems10.1002/int.2244336:8(3880-3916)Online publication date: 30-Jun-2021

Index Terms

  1. Cooperative Domain Ontology Reduction Based on Power Sets

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    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
    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 for this article.

    Check for updates

    Author Tags

    1. Attributes
    2. Inter-organizational domain ontology
    3. Ontology Reductio
    4. Power Sets

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICFET 2020

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 11 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2021)Semantic data mining in the information ageInternational Journal of Intelligent Systems10.1002/int.2244336:8(3880-3916)Online publication date: 30-Jun-2021

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media