@Article{jai.2020.09968,
AUTHOR = {Hangjun Zhou, Tingting Shen, Xinglian Liu, Yurong Zhang, Peng Guo, Jianjun Zhang},
TITLE = {Survey of Knowledge Graph Approaches and Applications},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {89--101},
URL = {http://www.techscience.com/jai/v2n2/39517},
ISSN = {2579-003X},
ABSTRACT = {With the advent of the era of big data, knowledge engineering has received
extensive attention. How to extract useful knowledge from massive data is the key to big
data analysis. Knowledge graph technology is an important part of artificial intelligence,
which provides a method to extract structured knowledge from massive texts and images,
and has broad application prospects. The knowledge base with semantic processing
capability and open interconnection ability can be used to generate application value in
intelligent information services such as intelligent search, intelligent question answering
and personalized recommendation. Although knowledge graph has been applied to various
systems, the basic theory and application technology still need further research. On the
basis of comprehensively expounding the definition and architecture of knowledge graph,
this paper reviews the key technologies of knowledge graph construction, including the
research progress of four core technologies such as knowledge extraction technology,
knowledge representation technology, knowledge fusion technology and knowledge
reasoning technology, as well as some typical applications. Finally, the future development
direction and challenges of the knowledge graph are prospected.},
DOI = {10.32604/jai.2020.09968}
}