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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2003: Developments in Applied Artificial Intelligence pp 11–20Cite as

Ontology-Based Knowledge Fusion Framework Using Graph Partitioning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2718))

Abstract

For a variety of knowledge sources and time-critical tasks, knowledge fusion seems to be a proper concern. In this paper, we proposed a reconstruction concept and a three-phase knowledge fusion framework which utilizes the shared vocabulary ontology and addresses the problem of meta-knowledge construction. In the framework, we also proposed relationship graph, an intermediate knowledge representation, and two criteria for the fusion process. An evaluation of the implementation of our proposed knowledge fusion framework in the intrusion detection systems domain is also given.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, National Chiao Tung University, Hsinchu, 300, Taiwan, R.O.C.

    Tsung-Ting Kuo, Shian-Shyong Tseng & Yao-Tsung Lin

Authors
  1. Tsung-Ting Kuo
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  2. Shian-Shyong Tseng
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  3. Yao-Tsung Lin
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Loughborough University, Loughborough, LE11 3TU, England

    Paul W. H. Chung  & Chris Hinde  & 

  2. Dept. of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666, USA

    Moonis Ali

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© 2003 Springer-Verlag Berlin Heidelberg

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Kuo, TT., Tseng, SS., Lin, YT. (2003). Ontology-Based Knowledge Fusion Framework Using Graph Partitioning. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_2

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  • DOI: https://doi.org/10.1007/3-540-45034-3_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40455-2

  • Online ISBN: 978-3-540-45034-4

  • eBook Packages: Springer Book Archive

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