With the growing usage of ontologies in many knowledge-intensive sectors, not only has the number of available ontologies increased considerably, but increasingly they are blowing up in size and becoming more complex to manage. Moreover, modelling domain knowledge in the form of ontologies is labour-intensive work which is expensive from an implementation perspective. There is therefore a strong demand for technologies and automated tools for creating restricted views of ontologies so that existing ontologies can be reused to their full potential. Forgetting is a non-standard reasoning service that seeks to create restricted views of ontologies by eliminating some terms from the ontologies in such a way that all logical consequences are preserved up to the remaining terms. It allows ontology engineers to focus on specific parts of (usually very large) ontologies that can be easily reused, or to zoom in on (usually very complex) ontologies for in-depth analysis of certain subparts. Despite its notable usefulness in ontology engineering, forgetting, on the other hand, is an inherently difficult problem; it is much harder than standard reasoning (satisfiability testing) and very few logics are known to be complete for forgetting. In this talk, I will present a practical approach to computing solutions of forgetting for several expressive description logics. The approach is terminating and sound, and although it is incomplete, a series of experimental evaluations with a prototype implementation of the approach have shown very good success rates on large corpora of real-world ontologies.
Dr. Yizheng Zhao
17.01.2019 - 10:15