首页|Data on Support Vector Machines Detailed by Researchers at Beijing University of Science and Technology (A multi-strategy ontology mapping method based on cost- sensitive SVM)
Data on Support Vector Machines Detailed by Researchers at Beijing University of Science and Technology (A multi-strategy ontology mapping method based on cost- sensitive SVM)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in support vector machines. According to news reporting out of Beijing University o f Science and Technology by NewsRx editors, research stated, “As the core of ont ology integration, the task of ontology mapping is to find the semantic relation ship between ontologies.” The news journalists obtained a quote from the research from Beijing University of Science and Technology: “Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregardi ng semantic nuances and necessitating substantial manual intervention. Therefore , this paper introduces an ontology mapping method. Based on the traditional ont ology mapping method, the process employs a deep learning model to mine the sema ntic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the s imilarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similari ty matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. How ever, since the number of mapped pairs is much larger than the number of non-map ped pairs, the number of positive samples in the data set is much smaller than t he number of negative samples.”
Beijing University of Science and Techno logyMachine LearningSupport Vector Machines
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