I have a domain ontology and I want to enrich it. I have extracted many concepts but these are too many. So I want to apply some clustering according to base ontology classes. Anyone can give me any idea in this regard?
Before to cluster any set of elements you need a similarity function to compare all of those elements. Fortunately, in this issue there are a lot of papers that can you use. The other part of the task is to apply a clustering algorithm, classical or ontology-based.
Maedche, A., Zacharias, V., 2002. Clustering Ontology-Based Metadata in the Semantic Web, in: Proc. of 6th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2002. Springer-Verlag, London, UK, pp. 348-360.
However, in your case, you want to compare a set of candidates (that are not in the ontology) with the existing ontology, and I think that you have to develop a new metric because those candidates are not part of the ontology itself.
if you can change you data into numbers I suggest to use neural networks with self organizing map
Amr Abdulfattah Hassan how can i convert words into numbers these words are not fix words....sorry i couldn't understand what you want to say?
@David Isern
can you please explain it "Before to cluster any set of elements you need a similarity function to compare all of those elements".
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