Advertisement

Construction and Merging of ACM and ScienceDirect Ontologies

  • M. PriyaEmail author
  • Ch. Aswani Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

An Ontology is an absolute formal conceptualization of some realm of significance. Nowadays Ontologies play a vibrant part in Information Architecture, Biomedical Informatics, Electronic commerce, Software Engineering, Semantic Web, Knowledge management, Artificial Intelligence and etc. Huge number of Ontologies and extensive variety of Ontologies are available for every single domain. It creates very difficult to maintain and access all the existing Ontologies. Ontology merging is the solution to overcome this kind of problems. Ontology merging is a procedure of fetching two existing Ontologies as input and obtains a newly merged Ontology as output. The merged Ontology will have common concepts and relationships between two Ontologies. This paper presents how two Ontologies can be constructed and merged using Protege and Conexp tools with an example.

Keywords

ACM Conexp Formal concept analysis Ontology Ontology construction Ontology merging Prompt Protégé ScienceDirect 

References

  1. 1.
    Li, S., Lu, Q., Li, W.: Experiments of ontology construction with formal concept analysis. In: International Joint Conference on Natural Language Processing, pp. 67–75 (2005)Google Scholar
  2. 2.
    Priya, M., Aswani Kumar, Ch.: A survey of state of the art of ontology construction and merging using formal concept analysis. Indian J. Sci. Technol. 8(24), 2–7 (2015)Google Scholar
  3. 3.
    Lian, Z.: A tool to support ontology creation based on incremental mini-ontology merging. Data Extraction Research Group, Department of Computer Science, Brigham Young University (2008)Google Scholar
  4. 4.
    de Bruijn, J., Ehrig, M., Feier, C., Martin-Recuerda, F., Scharffe, F., Weiten, M.: Ontology mediation, merging, and aligning. In: Davies, J., Studer, R., Warren, P. (eds.) Semantic Web Technologies: Trends and Research in Ontology-Based Systems, pp. 102–104. Wiley, New York (2006)Google Scholar
  5. 5.
  6. 6.
    Noy, N.F., Musen, M.A.: PROMPT: algorithm and tool for automated ontology merging and alignment. In: Proceedings of the AAAI 2000, pp. 1–6 (2000)Google Scholar
  7. 7.
    Malik, S.K., Prakash, N., Rizvi, S.A.M.: Ontology merging using prompt plug-in of protégé in semantic web. In: International Conference on Computational Intelligence and Communication Networks, pp. 476–481 (2010)Google Scholar
  8. 8.
    Ostrowski, D.A., Schleis, G.M.: Enterprise ontology merging for the semantic web. In: Proceedings of 2008 International Conference on Semantic Web and Web Services, WorldComp 2008, Las Vegas, Nevada, USA, 7–17 July (2008)Google Scholar
  9. 9.
  10. 10.
    Huang, Z., Van Harmelen, F., Teije, A.T., Groot, P., Visser, C.: Reasoning with inconsistent ontologies: a general framework. EU-IST Integrated Project (IP) IST-2003-506826 SEKT (2005)Google Scholar
  11. 11.
    Stephens, L.M., Gangam, A., Huhns, M.N.: Constructing consensus ontologies for the semantic web: a conceptual approach. World Wide Web: Internet Web Inf. Syst. 7, 421–442 (2004)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–136 (1996)CrossRefGoogle Scholar
  14. 14.
    Ganter, B., Stumme, G.: Creation and merging of ontology top-levels. In: International Conference on Conceptual Structures, vol. 2746, pp. 131–145, July 2003Google Scholar
  15. 15.
    Fan, Z., Zlatanova, S.: Exploring ontologies for semantic interoperability of data in emergency response. Appl. Geomat. 3(2), 109–122 (2011)CrossRefGoogle Scholar
  16. 16.
    Gómez-Pérez, A., Fernández-López, M.: Ontological Engineering: With Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. Springer, London (2010)Google Scholar
  17. 17.
    Li, X., Liu, G., Ling, A., Zhan, J., An, N., Li, L., Sha, Y.: Building a practical ontology for emergency response systems. In: 2008 International Conference on Computer Science and Software Engineering, vol. 4, pp. 222–225 (2008)Google Scholar
  18. 18.
    Aswani Kumar, Ch., Srinivas, S.: Concept lattice reduction using fuzzy k-means clustering. Expert Syst. Appl. 9(1), 2696–2704 (2010)Google Scholar
  19. 19.
    Aswani Kumar, Ch.: Fuzzy clustering based formal concept analysis for association rule mining. Appl. Artif. Intell. 26(3), 274–301 (2005)Google Scholar
  20. 20.
    Aswani Kumar, Ch., Dias, S.M., Vieira, N.J.: Knowledge reduction in formal contexts using non-negative matrix factorization. Math. Comput. Simul. 109, 46–63 (2015)Google Scholar
  21. 21.
    Aswani Kumar, Ch.: Mining association rules using non-negative matrix factorization and formal concept analysis. In: International Conference on Information Processing, vol. 157, no. 1, pp. 31–39 (2011)Google Scholar
  22. 22.
    Mouliswaran, S.C., Aswani Kumar, Ch., Chandrasekar, C.: Modeling Chinese wall access control using formal concept analysis. In: International Conference on Contemporary Computing and Informatics (IC3I) (2017)Google Scholar
  23. 23.
    Aswani Kumar, Ch., Srinivas, S.: Concept lattice reduction using fuzzy K-means clustering. Expert Syst. Appl. 37(3), 2696–2704 (2010)Google Scholar
  24. 24.
    Prem Kumar, S., Aswani Kumar, Ch.: Bipolar fuzzy graph representation of concept lattice. Inf. Sci. 288, 437–448 (2014)Google Scholar
  25. 25.
    Chunduri, R.K., Aswani Kumar, Ch.: Scalable formal concept analysis algorithm for large datasets using spark. J. Ambient Intell. Humanized Comput. 1–21 (2018)Google Scholar
  26. 26.
    Chunduri, R.K., Aswani Kumar, Ch.: HaLoop approach for concept generation in formal concept analysis. JIKM 17(3), 1850029 (2018)Google Scholar
  27. 27.
    Subramanian, C.M., Aswani Kumar, Ch., Chelliah, C.: Role based access control design using three-way formal concept analysis. Int. J. Mach. Learn. Cybern. 9(11), 1807–1837 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia

Personalised recommendations