Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

With the emergence of the Semantic Web several domain ontologies were developed, which varied not only in their structure but also in the natural language used to define them. The lack of an integrated view of all web nodes and the existence of heterogeneous domain ontologies drive new challenges in the discovery of knowledge resources which are relevant to a user’s request. New approaches have recently appeared for developing web intelligence and helping users avoid irrelevant results on the web. However, there remains some work to be done. This work makes a contribution by presenting an ANN-based ontology matching model for knowledge source discovery on the Semantic Web. Experimental results obtained on a real case study have shown that this model provides satisfactory responses.