Mapping ontology vertices to a line using hypergraph framework

Abstract

As a conceptual semantic tool, ontology is widely used in many disciplines such as genetics, nutrition, and social sciences. The key issues for ontology applications are similarity calculations and ontology alignment. In recent years, various machine learning methods and computational models have been widely used in ontology optimization and computation. The core idea is to map the entire ontology graph into one-dimensional data, such as on a real axis or on a natural number set. Through the analysis of the previous multi-dividing ontology algorithm, the technique of achieving dimensionality reduction comes from the pairwise comparison of the ontology sample vertices. The weakness of such tricks is that only two ontology vertices can be extracted for comparison at a time, which causes the number of vertex pairs to be compared in the optimization model to become very large as the totally ontology sample size increases. This paper proposes a new class of ontology learning strategies, which aims to arrange the ontology concepts into one-dimensional data according to the sequence of natural numbers. The ontology optimization model does not compare two ontology vertices, but compares a set of ontology vertices and calculates the weight of each vertex by means of random walk calculating. Each set of compared ontology vertices constitutes a hyperedge, and thus the ontology sample sets and the computational framework are represented by hypergraph and its associated bipartite graph. The algorithm proposed in this paper has potential guiding significance and theoretical value for engineering applications. In addition, two examples are presented to illustrate that our hypergraph based ontology learning algorithm is effective for a specific application background.

Keywords

• Ontology
• Similarity measure
• Hypergraph
• Hyperedge