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.
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Acknowledgements
This research was supported by the Natural Science Foundation of Jilin Province in 2016, “Research on large-scale ontology mapping mechanism in cloud computing environment” under Grant No. 20160101250JC; "13th Five-Year" science and technology research project of the Education Department of Jilin Province in 2017, “Ontology integration method based on cloud network” under Grant JJKH2017164KJ; Science and technology research project of Jilin Engineering Normal University in 2017.
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Feng, Y., Fan, L. Ontology semantic integration based on convolutional neural network. Neural Comput & Applic 31, 8253–8266 (2019). https://doi.org/10.1007/s00521-019-04043-w
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DOI: https://doi.org/10.1007/s00521-019-04043-w