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Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

Wei Gao

School of Information and Technology, Yunnan Normal University, Kunming 650500, P. R. China

E-mail Address: gaowei@ynnu.edu.cn

Corresponding author

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Linli Zhu

School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, P. R. China

E-mail Address: zhulinli@jsut.edu.cn

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, and

Kaiyun Wang

Department of Editorial, Kunming University, Kunming 650214, P. R. China

E-mail Address: kmwky@sina.cn

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https://doi.org/10.1142/S0218127415400349Cited by:36 (Source: Crossref)

Abstract

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

Keywords:

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