Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (5): 43-50.doi: 10.11896/jsjkx.200100129

Special Issue: Theoretical Computer Scinece

• Theoretical Computer Science • Previous Articles     Next Articles

Stability Analysis of Ontology Learning Algorithm in Decision Graph Setting

ZHU Lin-li1,2, HUA Gang2, GAO Wei3   

  1. 1 School of Computer Engineering,Jiangsu University of Technology,Changzhou,Jiangsu 213001,China
    2 School of Information and Control Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China
    3 School of Information,Yunnan Normal University,Kunming 650500,China
  • Received:2020-01-19 Online:2020-05-15 Published:2020-05-19
  • About author:ZHU Lin-li,born in 1975,Ph.D,senior engineer.His main research interests include artificial intelligence and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (51574232).

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Abstract: Traditional ontology algorithms use heuristic tricks to calculate semantic similarity.With the increasing amount of data processed by ontology,more and more machine learning technologies are applied to get ontology functions.Stability is a necessary condition for ontology learning algorithms which requires that there is no substantial influence on the obtained optimal ontology function if the ontology sample set is slightly changed.This paper studies the stability and corresponding statistical characteristics of ontology learning algorithms in the setting that the dependency relation of ontology samples are characterized by a graph.Firstly,the traditional PO and LTO uniform stability conditions are analyzed.Then,the extended uniform stability conditions Pk and LkO for large samples are proposed,and related theoretical results are obtained.Finally,two sample transformations (replacement ontology samples and delete ontology samples) are combined to bring forward the concept of combined uniform stability in setting of large ontology samples,and general results are yielded by using statistical learning theory.In addition,under various stability conditions,the generalized bounds of ontology learning algorithms that satisfy the m-independent condition are discussed.

Key words: Generalized bound, Machine learning, Ontology, Sample capacity, Stability

CLC Number: 

  • TP391
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