Ontology for knowledge management and improvement of data mining result

Nowadays, large bodies of data in different domains are collected and stored. An efficient extraction of useful knowledge from these data becomes a huge challenge. This leads to the need for developing distributed data mining techniques (DDM). Moreover, it creates a complex problem of the management of the mined results. To solve this problem, we propose the Knowledge Map Ontology (KMO) architecture that allows an efficient representation of knowledge to guide the users in the extraction of such knowledge. KMO uses repositories built from Ontologies. The distribution of this architecture is done according to Tree P2P (TreeP) because Ontologies are structured as trees. We show that this architecture is very efficient and necessary in the field, where knowledge is distributed, varied, and representing very large quantities of data.