Ontological knowledge management through hybrid unsupervised clustering techniques


Ching-Chieh, Kiu and Chien-Sing, Lee (2008) Ontological knowledge management through hybrid unsupervised clustering techniques. PROGRESS IN WWW RESEARCH AND DEVELOPMENT, 4976. pp. 499-510.

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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: Article
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 19 Sep 2011 08:18
Last Modified: 19 Sep 2011 08:18
URII: http://shdl.mmu.edu.my/id/eprint/2790


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