Distance computation of ontology vector for ontology similarity measuring and ontology mapping

Abstract

In recent years, various kinds of learning techniques are applied in ontology similarity measuring and ontology mapping algorithms. The essence of these learning tricks is attributed to obtaining obtain a score function which is employed for calculating the similarity between vertices. Since each vertex’s related information is denoted as a p dimensional vector, the similarity between ontology vertices is equivalent to determine the distance between their correspond vectors in the high dimensional space. In this paper, we raise a new ontology learning algorithm for ontology similarity measuring and ontology mapping by means of distance computation for ontology vectors. The optimal ontology distance function is learned in terms of regularization and first-order approaches. Then, two experiments using two different kinds of ontology convex function are presented. The result data show the effectiveness of our new ontology learning algorithm in special engineering applications.

Keywords:

• Ontology
• similarity measuring
• ontology mapping
• distance function

AMS Subject Classifications:

• 68T05
• 62J02

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant number 60903131]; Science and Technology of Jiangsu Province [grant number BE2011173]; and Key Laboratory of Computer Network and Information Integration Founding in Southeast University.