Ontology Vector Learning Scheming Using General Lasso Method and Applied to Multiple Disciplines
Linli Zhu, Xiaozhong Min, Wei Gao, Haixu Xi · International Journal of Control and Automation · 2017
In information retrieval and other computer applications, ontology acts as an effective role to retrieve the concepts that have highly semantic similarity with the original query concept, and return the results to the user.Ontology mapping is used to connect the relationship between different ontologies, and similarity computation is the essence of such applications.In this article, we present a new ontology sparse vector scheming for ontology similarity measure and ontology mapping in terms of general fusion lasso.The solution of ontology optimization problem is obtained via learning its Lagrangian version.The implementation procedure is based on gradient computating and fusion step, and the parameters in the ontology framework are chosen by means of cross-vaildation and bayesian information criterion.The simulation experiment results show that the newly proposed method has high efficiency and accuracy in ontology similarity measure and ontology mapping in multiple disciplines.