Open Access
ARTICLE
Construction and Application of Knowledge Graph for Quality and Safety Supervision of Transportation Engineering
Sheng Huang, Chuanle Liu*
Quality and Safety Supervision Bureau of Hunan Transportation Construction, Changsha, 410116, China
* Corresponding Author: Sheng Huang. Email:
Journal on Artificial Intelligence 2021, 3(4), 153-162. https://doi.org/10.32604/jai.2021.025175
Received 02 December 2021; Accepted 22 January 2022; Issue published 07 February 2022
Abstract
Knowledge graph technology play a more and more important role in
various fields of industry and academia. This paper firstly introduces the general
framework of the knowledge graph construction, which includes three stages:
information extraction, knowledge fusion and knowledge processing. In order to
improve the efficiency of quality and safety supervision of transportation
engineering construction, this paper constructs a knowledge graph by acquiring
multi-sources heterogeneous data from supervision of transportation engineering
quality and safety. It employs a bottom-up construction strategy and some natural
language processing methods to solve the problems of the knowledge extraction for
transportation engineering construction. We use the entity relation extraction method
to extract the entity triples from the multi-sources heterogeneous data, and then
employ knowledge inference to complete the edges in the constructed knowledge
graph, finally perform quality evaluation to add the valid triples to the knowledge
graph for updating. Subgraph matching technology is also exploited to retrieve the
constructed knowledge graph for efficiently acquiring the useful knowledge about
the quality and safety of transportation engineering projects. The results show that
the constructed knowledge graph provides a practical and valuable tool for the
quality and safety supervision of transportation engineering construction.
Keywords
Cite This Article
S. Huang and C. Liu, "Construction and application of knowledge graph for quality and safety supervision of transportation engineering,"
Journal on Artificial Intelligence, vol. 3, no.4, pp. 153–162, 2021. https://doi.org/10.32604/jai.2021.025175