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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. <\/jats:p>","DOI":"10.1142\/s0218127415400349","type":"journal-article","created":{"date-parts":[[2016,1,14]],"date-time":"2016-01-14T14:17:31Z","timestamp":1452781051000},"page":"1540034","source":"Crossref","is-referenced-by-count":36,"title":["Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology"],"prefix":"10.1142","volume":"25","author":[{"given":"Wei","family":"Gao","sequence":"first","affiliation":[{"name":"School of Information and Technology, Yunnan Normal University, Kunming 650500, P. R. 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