Computer Application Technology Department,China.
Petro China Research Institute of Petroleum Exploration & Development, Beijing 100083, China.

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Abstract—With the rapid developmentand allround popularization of artificial intelligence, all walks of life are also trying their best to promote the cross integration of information in different fields, using the Internet to promote industrial transformation, and promoting the transformation of industrial economy to information economy. Therefore, semantic understanding and text analysis are more and more indepth research in enterprise information intelligence. In order to meet the needs of effective analysis andprocessing a large amountof information data in key construction projects of large-scale energy enterprises, this paper proposes a natural language processing and semantic analysis model based on conceptual graphs design. The content of the unstructured data collected is studied by unsupervised machine learning according to the organization and representation knowledge in the conceptual graphs model, and then it is selfcontained the function of dynamic recognition of text semantics through text analyzer, and output the corresponding learning feedback results. The practical application results show that the design model is feasible, which significantly improves the learning effect and the accuracy of information screening, and also provides strong support for the follow-up big data analysis.

Index Terms— Machine reading comprehension, natural language processing, semantic analysis.

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Cite: Duanming Shen, Qing Li,Dexin Qiao, Xingwen Zhou , "Research on Natural Language Processing and Semantic Analysis Model Application Based on Conceptual Graphs," Journal of Software vol. 15, no. 2 pp. 45-52, 2020.

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