Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting - Linli Zhu; Gang Hua (IEEE Access, 2020)
The multi-dividing ontology learning framework has been proven to have a higher efficiency for tree-structured ontology learning, and in this work, we consider a special setting of this learning framework in which ontology sample set for each rate is divided into two groups. This setting can be regarded as the classic two-sample learning problem associated with multi-dividing ontology framework. In this work, we mainly focus on the theoretical analysis of multi-dividing two-sample ontology learning algorithm, whose ontology objective function is proposed, and the generalization bounds in this setting is obtained in terms of $U$ -statistics technique. The theoretical result given is of potential guiding significance in the field of ontology engineering applications.