Abstract
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula> -statistics technique. The theoretical result given is of potential guiding significance in the field of ontology engineering applications.
Highlights
T HE concept of ontology originally belongs to the category of western philosophy, which refers to the expression and summary of the objective existence at the logical level
Ontology began to be introduced in the artificial intelligence in the 1980s, with 20 years of development, and it has been widely recognized in this century which was defined by a clear formal specification of a shared conceptual model
Multi-dividing ontology learning framework has attracted the attention of scholars in the recent decade since it fits the ontology graph with tree structure
Summary
T HE concept of ontology originally belongs to the category of western philosophy, which refers to the expression and summary of the objective existence at the logical level. The definitions of ontologies given by experts in various fields are different from all angles, researchers agree that ontologies can clearly define the information concepts in the field, and the use of ontologies in specific fields can make each subject accessible [3]. After all the information related to the concept is numerically expressed, a multi-dimensional vector is used to encapsulate the representation, that is, each vertex is a fixed p-dimensional vector, and a learning model can be used to learn various ontology graphs [15]. Set G = (V, E) as an ontology graph whose vertex set corresponds to concepts and edge set reveals the set of directly relationship between two concepts.
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