Enterprise industry classification method based on domain ontology and system

Abstract

The invention provides an enterprise industry classification method based on a domain ontology. The method comprises the steps that (1) constructing a category feature word bank through the domain ontology, and forming a feature extension set used for extending feature words of a short text; (2) extracting category feature words from annotation information of National Economy Industry Classification Annotation (edition 2017) by using a TF-IDF feature extraction method for representing basic features of each industry category, and forming a feature corpus in a vector form; (3) extracting main business keywords of an enterprise, removing useless words, performing feature extension on the feature keywords by using the feature extension set of the domain ontology, and strengthening feature information of the feature keywords to obtain extended to-be-classified short texts; and (4) performing classification operation on the to-be-classified short texts subjected to feature extension by utilizing a BM25 classification model, and determining the industry category to which the to-be-classified texts belong according to the text similarity. The invention further comprises a system for implementing the enterprise industry classification method based on the domain ontology. The problems that time and labor are wasted and work is tedious in manual classification are solved.

Description

Background technique [0001] Economic industry classification refers to the standard classification of economic activities in the whole society according to the national economic industry classification standards and according to certain principles and classification methods. As my country's economic development has entered a new normal, the economic structure has been continuously optimized, emerging industries such as new energy, new materials, and new medicines have developed rapidly, and new technologies such as artificial intelligence, the Internet of Things, and robots have emerged vigorously, and industry classifications have become more complex. At the same time, since most statistical staff have not participated in professional industry classification work, are not familiar with industry standards and lack practical experience, it is difficult to accurately determine the industry category of an enterprise, which virtually increases the difficulty of industry classificat...

AI Technical Summary

Problems solved by technology

Due to the short text length and sparse features, it is difficult to calculate the correlation between texts. The above-mentioned more commonly used text classification methods are applied to short text classification, and usually cannot obtain a better classification result.

[0002] Therefore, in order to solve the cumbersom...

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