Using hamming similarity to map ontology learning
Choukri Djellali · 2013
The best known approaches to learning ontologies from unstructured text focus on the extraction of ontologies by applying the techniques from natural language processing and machine learning. In the present study, we propose a semiautomatic approach that uses the variables selection and clustering to find the candidate changes. The model found in the training set is used to classify the new examples and to derive candidate changes. Our approach uses an alignment process to compare the ontological entities and candidate changes. The results show that the conceptual model is critically dependence on the measures distance. Good experimental studies demonstrate the multidisciplinary applications of our approach.