In this paper, we present an algorithm and a tool for computing minimal, equivalent EL concepts wrt. a given ontology. Our tool can provide valuable support in manual development of ontologies and improve the quality of ontologies automatically generated by processes such as uniform interpolation, ontology learning, rewriting ontologies into simpler DLs, abduction and knowledge revision. Deciding whether there exist equivalent EL concepts of size less than k is known to be an NP-complete problem. We propose a minimisation algorithm that achieves reasonable computational performance also for larger ontologies and complex concepts. We evaluate our tool on several bio-medical ontologies with promising results.

EL is a popular description logic, used as a core formalism in large existing knowledge bases. Uniform interpolants of knowledge bases are of high interest, e.g. in scenarios where a knowledge base is supposed to be partially reused. However, to the best of our knowledge no procedure has yet been proposed that computes uniform EL interpolants of general EL terminologies. Up to now, also the bound on the size of uniform EL interpolants has remained unknown. In this article, we propose an approach to computing a finite uniform interpolant for a general EL terminology if it exists. To this end, we develop a quadratic representation of EL TBoxes as regular tree grammars. Further, we show that, if a finite uniform EL interpolant exists, then there exists one that is at most triple exponential in the size of the original TBox, and that, in the worst case, no smaller interpolants exist, thereby establishing tight worst-case bounds on their size. Beyond showing these bounds, the notions and results established in this paper also provide useful insights for designing efficient ontology reformulation algorithms, for instance, within the context of module extraction.

As the amount of available ontologies and their size grow, ontology reuse gains in importance. However, the online available formalized knowledge in many cases need a revision which can lead to a high manual effort. In this paper, we propose an approach to support the revision of ontologies. We show that our method reduces the manual effort measured in number of decisions that have to be made by an ontology engineer by up to 83%.