Affiliations: Faculty of Information Technology, College of Sciences, Hue University, 77 Nguyen Hue, Hue city, Vietnam | Faculty of Information Technology, VNU University of Engineering and Technology, 144 Xuan Thuy, Hanoi, Vietnam | Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland | Institute of Mathematics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland. [email protected]
Note: [] Address for correspondence: Institute of Mathematics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland
Abstract: Concept learning in description logics (DLs) is similar to binary classification in traditional machine learning. The difference is that in DLs objects are described not only by attributes but also by binary relationships between objects. In this paper, we develop the first bisimulation-based method of concept learning in DLs for the following setting: given a knowledge base KB in a DL, a set of objects standing for positive examples and a set of objects standing for negative examples, learn a concept C in that DL such that the positive examples are instances of C w.r.t. KB, while the negative examples are not instances of C w.r.t. KB. We also prove soundness of our method and investigate its C-learnability.
