Abstract
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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Kiu, CC., Lee, CS. (2008). Ontological Knowledge Management Through Hybrid Unsupervised Clustering Techniques. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds) Progress in WWW Research and Development. APWeb 2008. Lecture Notes in Computer Science, vol 4976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78849-2_50
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DOI: https://doi.org/10.1007/978-3-540-78849-2_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78848-5
Online ISBN: 978-3-540-78849-2
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