Bisimulation-Based Concept Learning in Description Logics

• Autorzy: Tran T.-L. , Ha Q.-T. , Hoang T.-L.-G. , Nguyen L. A. , Nguyen H. S.

Identyfikatory

DOI

10.3233/FI-2014-1077

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

bisimulation; concept learning; description logics; machine learning; binary system

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2014
• Tom: Vol. 133, nr 2/3
• Strony: 287--303
• Opis fizyczny: Bibliogr. 18 poz., rys.

Twórcy

autor

Tran T.-L.

• Faculty of Information Technology, College of Sciences, Hue University, 77 Nguyen Hue, Hue city, Vietnam

autor

Ha Q.-T.

• Faculty of Information Technology, VNU University of Engineering and Technology, 144 Xuan Thuy, Hanoi, Vietnam

autor

Hoang T.-L.-G.

• Faculty of Information Technology, College of Sciences, Hue University, 77 Nguyen Hue, Hue city, Vietnam

autor

Nguyen L. A.

• Institute of Informatics University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

autor

Nguyen H. S.

• Institute of Mathematics University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

Bibliografia

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