Ontology semantic integration based on convolutional neural network

Abstract

As the most direct media form in the Internet age, text information has received more and more attention in the era of the maturity of social networks and the rapid development of mobile terminals technology (general name). In this paper, the methods of removing invalid records, manually filling in vacancy values and using global constant to fill in vacancy values are used to preprocess semantic ontology. Then, the semantic ontology is further processed by using Chinese word segmentation to reduce the computational cost of later feature extraction and to increase the effectiveness of feature extraction. Using X2 statistic $${\text{CHI(}}t,x)$$ to extract the feature of ontology semantics, the high-dimensional data are mapped to low-dimensional space by transformation, which reduces the dimension of vector space and simplifies the calculation. Finally, using news data source and MATLAB software design platform to do classification experiment using convolutional neural network classification and from recall rate, correct rate and F-measure, three indicators to compare the classification effect of NB, KNN, SVM, the effectiveness of the experimental method is verified.