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International Conference on Neural Information Processing

ICONIP 2006: Neural Information Processing pp 295–301Cite as

Ontology Learning from Text: A Soft Computing Paradigm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

Abstract

Text-based information accounts for more than 80% of today’s Web content. They consist of Web pages written in different natural languages. As the semantic Web aims at turning the current Web into a machine-understandable knowledge repository, availability of multilingual ontology thus becomes an issue at the core of a multilingual semantic Web. However, multilingual ontology is too complex and resource intensive to be constructed manually. In this paper, we propose a three-layer model built on top of a soft computing framework to automatically acquire a multilingual ontology from domain specific parallel texts. The objective is to enable semantic smart information access regardless of language over the Semantic Web.

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References

  1. Benjamins, V.R., Contreras, J., Corcho, O., Gómez-Pérez, A.: Six Challenges for the Semantic Web. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, Springer, Heidelberg (2002), http://iswc2002.semanticweb.org/posters.html

    Google Scholar 

  2. Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 34–43 (2001)

    Article  Google Scholar 

  3. Buitelaar, P., Cimiano, P., Magnini, B. (eds.): Ontology learning from text: Methods, evaluations and applications. IOS Press, Amsterdam (2005)

    Google Scholar 

  4. Dittenbach, M., Rauber, A., Merkl, D.: Uncovering hierarchical structure in data using the growing hierarchical self-organizing map. Neurocomputing 48, 199–216 (2002)

    Article  MATH  Google Scholar 

  5. Kohonen, T.: Self-Organising Maps. Springer, Berlin (1995)

    Google Scholar 

  6. Miyamoto, S.: Fuzzy sets in information retrieval and cluster analysis. Kluwer Academic Publishers, Dordrecht (1990)

    MATH  Google Scholar 

  7. Zadeh, L.A.: Similarity relations and fuzzy orderings. Information Sciences 3, 177–206 (1971)

    Article  MATH  MathSciNet  Google Scholar 

  8. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Clayton School of Information Technology, Faculty of Information Technology, Monash University, Clayton, Victoria, 3800, Australia

    Rowena Chau & Chung-Hsing Yeh

  2. School of Engineering and Information Technology, 221 Burwood Highway, Burwood, Victoria, 3125, Australia

    Kate Smith-Miles

Authors
  1. Rowena Chau
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  2. Kate Smith-Miles
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  3. Chung-Hsing Yeh
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, New Territories,, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Chau, R., Smith-Miles, K., Yeh, CH. (2006). Ontology Learning from Text: A Soft Computing Paradigm. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_34

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  • DOI: https://doi.org/10.1007/11893295_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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