@Article{jai.2021.025175,
AUTHOR = {Sheng Huang, Chuanle Liu},
TITLE = {Construction and Application of Knowledge Graph for Quality and Safety Supervision of Transportation Engineering},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {153--162},
URL = {http://www.techscience.com/jai/v3n4/46712},
ISSN = {2579-003X},
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.},
DOI = {10.32604/jai.2021.025175}
}