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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kiu, C. C., & Lee, C. S. (2008). Ontological knowledge management through hybrid unsupervised clustering techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4976 LNCS, pp. 499–510). https://doi.org/10.1007/978-3-540-78849-2_50
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