A Formalization of Ontology Learning From Text

Recent developments towards knowledge-based applications in general and Semantic Web applications in particular are leading to an increased interest in ontologies and in dynamic methods for developing and maintaining them. As human language is a primary mode of knowl- edge transfer, ontology learning from relevant text collections has been among the most successful strategies in this work. Such methods mostly combine a certain level of linguistic analysis with statistical and/or ma- chine learning approaches to find potentially interesting concepts and relations between them. Here, we discuss a formalization of this process (in the specific context of the OntoLT tool for ontology learning from text) in order to arrive at a better definition of this task, which we hope to be of use in a more principled comparison of dierent approaches. As ontology representation formalisms we will consider those that have a model-theoretic semantics, with OWL (and subsets of OWL) being appropriate candidates.