Theoretical analysis of perturbation multi-dividing ontology learning algorithm

Volume 39, Issue 3, pp 325--337 https://dx.doi.org/10.22436/jmcs.039.03.02

Publication Date: April 08, 2025 Submission Date: May 08, 2024 Revision Date: July 29, 2024 Accteptance Date: February 28, 2025

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Authors

W. Gao - School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China. J. Zhou - Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming 650500, China.

Abstract

The multi-dividing ontology learning algorithm is specially designed for tree-structured ontology graphs, and has become a paradigm of graph-based ontology learning. In view of the disturbance of ontology data, this paper proposes perturbation multi-dividing ontology learning approach. Assuming that the perturbed ontology data are drawn from the same distribution as before, the error bound of perturbation multi-dividing ontology learning is given in such hypothesis. Finally, we analyze flaws in theoretical results and gaps with practical applications, and raise the open problem for future study.

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Keywords

• Ontology
• similarity measuring
• perturbation multi-dividing ontology learning algorithm

MSC

•  68Q32
•  68T05

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