Collaborative knowledge capture in ontologies
Proceedings of the 3rd international conference on Knowledge capture
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Knowledge discovery through composited visualization, navigation and retrieval
DS'05 Proceedings of the 8th international conference on Discovery Science
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Review: Formal concept analysis in knowledge processing: A survey on applications
Expert Systems with Applications: An International Journal
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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.