Design of Knowledge Map Construction Based on Convolutional Neural Network.

Li, Xiulai; Chen, Mingrui; Xie, Gengquan; Jiang, Yirui

With the advent of the Web era, data has exploded, with tens of thousands of textual data being generated every day. Traditional text sentiment analysis methods are mainly based on lexicon and machine learning-based methods. These methods show certain limitations as the data size increases. Here, we propose a new knowledge map design method based on convolutional neural network. For the academic literature data crawled from HowNet and Baidu Academic Website with the theme of computer, the corresponding ontology database, the fusion application of multiple data sources, and the mapping of different ontology libraries through data fusion are constructed for data sources in different fields. The global ontology library then uses the entity alignment and entity link methods for knowledge acquisition and fusion. Finally, the convolutional neural network is used for training and testing. The experimental results show that the subject search task can not only obtain the book through the convolution network, the effective academic literature in the question bank can also be used to obtain the relevance of the keyword in the search results, which verifies the effectiveness of the method.

DATA libraries; KEYWORD searching; CONVOLUTIONAL neural networks; KNOWLEDGE acquisition (Expert systems); DATA fusion (Statistics); SENTIMENT analysis

International Journal of Pattern Recognition & Artificial Intelligence, 2019, Vol 33, Issue 12, pN.PAG

0218-0014

Academic Journal

10.1142/S021800141951008X