Information Extraction for Learning Expressive Ontologies

Ontologies are used to represent knowledge in a formal and unambiguous way, facilitating its reuse and sharing among people and computer systems. A large amount of knowledge is traditionally available in unstructured text sources and manually encoding their content into a formal representation is costly and time-consuming. Several methods have been proposed to support ontology engineers in the ontology building process, but they mostly turned out to be inadequate for building rich and expressive ontologies. We propose some concrete research directions for designing an effective methodology for semi-supervised ontology learning. This methodology will integrate a new axiom extraction technique which exploits several features of the text corpus.