tailieunhanh - Doctoral thesis summary: Concept learning for description logic based information systems

Research objectives: Building a method of granulating partitions of domain of interpretation in DLs. This method is base on bisimulation and using suitable selectors as well as information gain measure; proposing bisimulation-based concept learning algorithms for knowledge bases in DLs using Setting. | HUE UNIVERSITY COLLEGE OF SCIENCES TRAN THANH LUONG CONCEPT LEARNING FOR DESCRIPTION LOGIC-BASED INFORMATION SYSTEMS MAJOR COMPUTER SCIENCE CODE SUMMARY OF DOCTORAL THESIS OF COMPUTER HUE 2015 This thesis was completed at College of Sciences Hue University Supervisors 1. Assoc. Prof. . Nguyen Anh Linh Warsaw University Poland 2. Dr. Hoang Thi Lan Giao College of Sciences Hue University Reviewer 1 Prof. . Hoang Van Kiem University of Information Technology VNU-HCM Reviewer 2 Assoc. Prof. Dr. Doan Van Ban Institute of Information Technology VAST Reviewer 3 Assoc. Prof. Dr. Nguyen Mau Han College of Sciences Hue University The thesis will be presented at the Committee of Hue University to be held by Hue University at .h. . . 2015 The thesis can be found at the follow libraries National Library of Vietnam Library Information Center College of Science Hue University PREFACE Concept learning in description logics DLs is similar to binary classification in traditional machine learning. However the problem in the context of DLs differs from the traditional setting in that objects are described not only by attributes but also by binary relations between objects. The major settings of concept learning in DLs are as follows Setting 1 Given a knowledge base KB in a DL LS and sets E E of individuals learn a concept C in Ls such that 1. KB 1 C a for all a 2 E and 2. KB C a for all a 2 E-. The sets E and E_ contain positive examples and negative ones of C respectively. Setting 2 This setting differs from the previous one only in that the second condition is replaced by the weaker one KB C a for all a 2 E_. Setting 3 Given an interpretation I and sets E E of individuals learn a concept C in a DL LS such that 1. I C a for all a 2 E and 2. I C a for all a 2 E_. Note that I C a is the same as I C a . Concept learning in DLs was studied by a number of researchers. The related work can be divided into three main groups. The first group focuses on learnability in .