Thanh-Luong Tran

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We develop the first bisimulation-based method of concept learning, called BBCL, for knowledge bases in description logics (DLs). Our method is formulated for a large class of useful DLs, with well-known DLs like <i>ALC, SHIQ, SHOIQ, SROIQ</i>. As bisimulation is the notion for characterizing indis-cernibility of objects in DLs, our method is natural and(More)
—The work [1] by Nguyen and Szałas 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(More)
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:(More)
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(More)
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