An Approach for Chart Description Generation in Cyber–Physical–Social System

Abstract

There is an increasing use of charts generated by the social interaction environment in manufacturing enterprise applications. To transform these massive amounts of unstructured chart data into decision support knowledge for demand-capability matching in manufacturing enterprises, we propose a manufacturing enterprise chart description generation (MECDG) method, which is a two-phase automated solution: (1) extracting chart data based on optical character recognition and deep learning method; (2) generating chart description according to user input based on natural language generation method and matching the description with extracted chart data. We verified and compared the processing at each phase of the method, and at the same time applied the method to the interactive platform of the manufacturing enterprise. The ultimate goal of this paper is to promote the knowledge extraction and scientific analysis of chart data in the context of manufacturing enterprises, so as to improve the analysis and decision-making capabilities of enterprises.