Semantics are added to content components through ontological definitions to provide context to learning objects (LOs). Therefore, an ontological contextual environment facilitates knowledge management processes such as reusing, sharing, retrieving and indexing LOs for contextual learning in integrated learning environments. Consequently, contextual LOs from different learning object repositories can be more easily and meaningfully codified and exchanged through a shared ontology. This paper presents new ontological mapping and merging results using a hybrid data mining approach in our ontology mapping and merging method, OntoDNA. Different lexical measures are used to discover semantic similarity between ontological elements to generate a shared ontology. Accuracy in mapping and merging is measured using precision, recall, and f-measure. Significance of the study lies in the algorithm?s scalability and in simple transformation of ontological attributes for data processing.