Ontological knowledge management through hybrid unsupervised clustering techniques

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 uns...

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Main Authors: Ching-Chieh, Kiu, Chien-Sing, Lee
Format: Article
Published: SPRINGER-VERLAG BERLIN 2008
Subjects:
Online Access:http://shdl.mmu.edu.my/2790/
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author Ching-Chieh, Kiu
Chien-Sing, Lee
author_facet Ching-Chieh, Kiu
Chien-Sing, Lee
author_sort Ching-Chieh, Kiu
building MMU Institutional Repository
collection Online Access
description 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.
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spelling mmu-27902011-09-19T08:18:15Z http://shdl.mmu.edu.my/2790/ Ontological knowledge management through hybrid unsupervised clustering techniques Ching-Chieh, Kiu Chien-Sing, Lee T Technology (General) QA75.5-76.95 Electronic computers. Computer science 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. SPRINGER-VERLAG BERLIN 2008-04 Article NonPeerReviewed 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. http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=1&SID=V2GaBj7ab9nE8K6j8mn&page=83&doc=828
spellingShingle T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
Ching-Chieh, Kiu
Chien-Sing, Lee
Ontological knowledge management through hybrid unsupervised clustering techniques
title Ontological knowledge management through hybrid unsupervised clustering techniques
title_full Ontological knowledge management through hybrid unsupervised clustering techniques
title_fullStr Ontological knowledge management through hybrid unsupervised clustering techniques
title_full_unstemmed Ontological knowledge management through hybrid unsupervised clustering techniques
title_short Ontological knowledge management through hybrid unsupervised clustering techniques
title_sort ontological knowledge management through hybrid unsupervised clustering techniques
topic T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/2790/
http://shdl.mmu.edu.my/2790/