Bisimulation-based concept learning for information systems in description logics

Abstract

In description logic-based information systems, objects are described not only by attributes but also by binary relations between them. This work studies concept learning in such information systems. It extends the bisimulation-based concept learning method of Nguyen and Szałas (Rough sets and intelligent systems. Springer, Berlin, pp 517–543, 2013 ). We take attributes as basic elements of the language. Each attribute may be discrete or numeric. A Boolean attribute is treated as a concept name. This approach is more general and suitable for practical information systems based on description logic than the one of Nguyen and Szałas (Rough sets and intelligent systems. Springer, Berlin, pp 517–543, 2013 ). As further extensions, we also allow data roles and the concept constructors “functionality” and “unqualified number restrictions”. We formulate and prove an important theorem on basic selectors. We also present a domain partitioning method based on information gain that has been used for our implementation of the method. Apart from basic selectors and simple selectors, we introduce a new kind of selectors, called extended selectors. The evaluation results show that our concept learning method is valuable and extended selectors support it significantly.