Advertisement

Enriching Existing Ontology Using Semi-automated Method

  • Md. Jabed Hasan
  • Amna Islam Badhan
  • Nafiz Ishtiaque Ahmed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)

Abstract

Ontology is a kind of philosophical study which is dealing with nature being. Ontologies are extremely useful tools for different purpose and various modalities in different areas and communities. A common ontology is very effective in sophisticated software engineering purpose. In realistic world new meaningful words are always improving a language and to enhance the most widely used ontologies it requires mapping. To assure the quality manual mapping is used with some limitation. Partial automated mapping may apply to extend ontology by extracting and integrating knowledge from existing resources more effectively. In this paper, we present a semi-automated method, type of machine learning to enrich an existing ontology. Moreover, the approach can save time and ensure the accuracy that they need to serve.

Keywords

Ontology Mapping Semi-automated method and machine learning 

Notes

Acknowledgment

First of all we would like to show our gratitude to the Almighty, who gave us the effort to work on this project. We want to thanks our honorable supervisor Bayzid Ashik Hossain for guiding us. His profound knowledge in this field, keen interest, patience and continuous support lead to the completion of our work. His instructions have contributed greatly in every aspect of the thesis.

References

  1. 1.
    Faria, D., Pesquita, C., Santos, E., Cruz, I.F., Couto, F.M.: Automatic background knowledge selection for matching biomedical ontologies. PLoS ONE 9(11), e111226 (2014)CrossRefGoogle Scholar
  2. 2.
    Berndt, D.J., McCart, J.A., Luther, S.L.: Using ontology network structure in text mining, pp. 41–45 (2010)Google Scholar
  3. 3.
    Maltese, V., Hossain, B.A.: SAM: a tool for the semiautomatic mapping and enrichment of ontologies (2012)CrossRefGoogle Scholar
  4. 4.
    Gella, S., Strapparava, C., Nastase, V.: Mapping WordNet domains, WordNet topics and wikipedia categories to generate multilingual domain specific resources (2014)Google Scholar
  5. 5.
    Caro, L.D., Boella, G.: Automatic enrichment of WordNet with common-sense knowledge (2016)Google Scholar
  6. 6.
    Elbedweihy, K., Wrigley, S.N., Ciravegna, F., Reinhard, D., Bernstein, A.: Evaluating semantic search systems to identify future directions of research (2012)Google Scholar
  7. 7.
    Choi, N., Song, I., Han, H.: A survey on ontology mapping. ACM SIGMOD Rec. 35, 34–41 (2006)CrossRefGoogle Scholar
  8. 8.
    Gaeta, M., Orciuoli, F., Ritrovato, P.: Advanced ontology management system for personalized e-Learning. Knowl. Based Syst. 22(4), 292–301 (2009)CrossRefGoogle Scholar
  9. 9.
    Varelas, G., Voutsakis, E., Raftopulou, P., Petrakis, E.G., Milios, E.E.: Semantic similarity methods in wordNet and their application to information retrieval on the web (2005)Google Scholar
  10. 10.
    Lei, Y., Uren, V., Motta, E.: SemSearch: a search engine for the semantic web (2016)Google Scholar
  11. 11.
    Shamsfard, M., Hesabi, A., Fadaei, H., Mansoory, N., Famian, A., Bagherbeigi, S., Fekri, E., Monshizadeh, M., Assi, S.M.: Semi automatic development of FarsNet; The Persian WordNet (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Md. Jabed Hasan
    • 1
  • Amna Islam Badhan
    • 1
  • Nafiz Ishtiaque Ahmed
    • 1
  1. 1.Department of Computer ScienceAmerican International University – BangladeshDhakaBangladesh

Personalised recommendations