Next Article in Journal
Atom Probe Tomography for Catalysis Applications: A Review
Next Article in Special Issue
Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives
Previous Article in Journal
Digital Image Correlation Applications in Composite Automated Manufacturing, Inspection, and Testing
Previous Article in Special Issue
Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution
Article Menu
Issue 13 (July-1) cover image

Export Article

Open AccessArticle

Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning

School of Software, Yunnan University, Yunnan 650500, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(13), 2720; https://doi.org/10.3390/app9132720
Received: 26 May 2019 / Revised: 29 June 2019 / Accepted: 2 July 2019 / Published: 5 July 2019
  |  
PDF [2646 KB, uploaded 5 July 2019]
  |     |  

Abstract

The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field. View Full-Text
Keywords: social network; industry 4.0; industrial knowledge graph; deep learning; industrial big data; intellectualization of industrial information social network; industry 4.0; industrial knowledge graph; deep learning; industrial big data; intellectualization of industrial information
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhao, M.; Wang, H.; Guo, J.; Liu, D.; Xie, C.; Liu, Q.; Cheng, Z. Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning. Appl. Sci. 2019, 9, 2720.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top