Contextual Hierarchy Driven Ontology Learning
Lobna Karoui · IGI Global eBooks · 2010
Research in ontology learning had always separated between ontology building and evaluation tasks. Moreover, it had used for example a sentence, a syntactic structure or a set of words to establish the context of a word. However, this research avoids accounting for the structure of the document and the relation between the contexts. In our work, we combine these elements to generate an appropriate context definition for each word. Based on the context, we propose an unsupervised hierarchical clustering algorithm that, in the same time, extracts and evaluates the ontological concepts. Our results show that our concept discovery approach improves the conceptual quality and the relevance of the extracted ontological concepts, provides a support for the domain experts and facilitates the evaluation task for them.