Bisimulation-Based Concept Learning in Description Logics

被引：13

作者：

Thanh-Luong Tran ^{[1
]}

Quang-Thuy Ha ^{[2
]}

Thi-Lan-Giao Hoang ^{[1
]}

Linh Anh Nguyen ^{[3
]}

Hung Son Nguyen ^{[4
]}

机构：

[1] Hue Univ, Coll Sci, Fac Informat Technol, Hue City, Vietnam

[2] VNU Univ Engn & Technol, Fac Informat Technol, Hanoi, Vietnam

[3] Univ Warsaw, Inst Informat, PL-02097 Warsaw, Poland

[4] Univ Warsaw, Inst Math, PL-02097 Warsaw, Poland

来源：

FUNDAMENTA INFORMATICAE | 2014年 / 133卷 / 2-3期

关键词：

D O I：

10.3233/FI-2014-1077

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：287 / 303

页数：17

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