Concept Learning for Description Logic-Based Information Systems

The work [1] by Nguyen and Szalas is a pioneering one that uses bisimulation for machine learning in the context of description logics. In this paper we generalize and extend their concept learning method [1] for description logic-based information systems. 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 much more suitable for practical information systems based on description logic than the one of [1]. As further extensions we allow also data roles and the concept constructors "functionality" and "unquantified number restrictions". We formulate and prove an important theorem on basic selectors. We also provide new examples to illustrate our approach.