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.
Highlights
The cyber–physical–social system (CPSS) [1,2,3] has recently attracted great attention as a new computing paradigm [4] that integrates physical systems, social systems, and information systems with development of intelligent computing, control, and communication technologies
We review the related works in chart data extraction and natural language generation technology
We mainly evaluate the manufacturing enterprise chart description generation (MECDG) method applied in manufacturing enterprises of the environment from two aspects of practicality and effectiveness
Summary
The cyber–physical–social system (CPSS) [1,2,3] has recently attracted great attention as a new computing paradigm [4] that integrates physical systems, social systems, and information systems with development of intelligent computing, control, and communication technologies. The increasing use of system in CPSS environment has resulted in explosion of data Among these data, unstructured chart data occupies a large proportion, but it is difficult to use traditional data analysis methods to process, due to its irregular data structure. This situation leaves two challenges for analysis and application of chart data. The original information to construct the chart, and it is difficult for machines to understand the text and data in the chart like human vision. Due to the diversity of chart types and the complexity of the image structure, the extraction of chart data is a problem worthy of attention. The knowledge in chart is the data and text information that can be directly observed and the specific trends and other relevant characteristics of the chart itself, which is the high-level information [12,13] that can convey more important and internal knowledge
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
