General Terminology Induction in OWL

Automated acquisition, or learning, of ontologies has attracted researchattention because it can help ontology engineers build ontologies and givedomain experts new insights into their data. However, existing approaches to ontology learning are considerably limited, e.g. focus on learning descriptions forgiven classes, require intense supervision and human involvement, make assumptions about data, do not fully respect background knowledge. We investigate the problem of general terminology induction, i.e. learning sets of general class inclusions, GCIs, from data and background knowledge. We introduce measuresthat evaluate logical and statistical quality of a set of GCIs. We present methodsto compute these measures and an anytime algorithm that induces sets of GCIs.Our experiments show that we can acquire logically and statistically sound setsof GCIs and provide insights into the structure of the search space.