Ontological Knowledge Management Through Hybrid Unsupervised Clustering Techniques


Kiu, Ching-Chieh and Lee, Chien-Sing (2008) Ontological Knowledge Management Through Hybrid Unsupervised Clustering Techniques. In: Progress in WWW Research and Development. Lecture Notes in Computer Science, 4976 (4976). Springer Link, pp. 499-510. ISBN 978-3-540-78848-5

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In the Semantic Web, ontology plays a prominent role to actualize knowledge sharing and reuse among distributed knowledge sources. Intelligently managing ontological knowledge (classes, properties and instances) enables efficacious ontological interoperability. In this paper, we present a hybrid unsupervised clustering model, which comprises of Formal Concept Analysis, Self-Organizing Map and K-Means for managing ontological knowledge, and lexical matching based on Levenshtein edit distance for retrieving knowledge. The ontological knowledge management framework supports the tasks of adding a new ontological concept, updating and editing an existing ontological concept and querying ontological concepts to facilitate knowledge retrieval through conceptual clustering, cluster-based identification and concept-based query. The framework can be used to facilitate ontology reuse and ontological concept visualization and navigation in concept lattice form through the formal context space.

Item Type: Book Section
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 14 Nov 2013 07:14
Last Modified: 14 Nov 2013 07:14
URII: http://shdl.mmu.edu.my/id/eprint/4411


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