General Terminology Induction in OWL

Abstract. Automated acquisition, or learning, of ontologies has attracted re-search attention because it can help ontology engineers build ontologies and give domain experts new insights into their data. However, existing approaches to on-tology learning are considerably limited, e.g. focus on learning descriptions for given classes, require intense supervision and human involvement, make assump-tions about data, do not fully respect background knowledge. We investigate the problem of general terminology induction, i.e. learning sets of general class in-clusions, GCIs, from data and background knowledge. We introduce measures that evaluate logical and statistical quality of a set of GCIs. We present methods to compute these measures and an anytime algorithm that induces sets of GCIs. Our experiments show that we can acquire interesting sets of GCIs and provide insights into the structure of the search space.

General Terminology Induction in OWL

Abstract. Automated acquisition, or learning, of ontologies has attracted re-search attention because it can help ontology engineers build ontologies and give domain experts new insights into their data. However, existing approaches to on-tology learning are considerably limited, e.g. focus on learning descriptions for given classes, require intense supervision and human involvement, make assump-tions about data, do not fully respect background knowledge. We investigate the problem of general terminology induction, i.e. learning sets of general class in-clusions, GCIs, from data and background knowledge. We introduce measures that evaluate logical and statistical quality of a set of GCIs. We present methods to compute these measures and an anytime algorithm that induces sets of GCIs. Our experiments show that we can acquire interesting sets of GCIs and provide insights into the structure of the search space.