On Combining Unsupervised Classification and Ontology Knowledge

This paper presents a way to combine knowledge obtained from a clustering algorithm and from an ontology. Using the both sources of information allows to improve the results of the knowledge discovery process. The basic property of clustering algorithms, which is to group similar objects, is the key of this approach. We use it to extend the knowledge given by an ontology. Indeed, this knowledge can be partial or not enough accurate, and clustering can then be used to fill this lack of information. We also present results and validation in the field of remote sensing image interpretation.