Open AccessKnowledge Graphs: Introduction, History and, Perspectives
Author(s) -
Vinay K. Chaudhri,
Chaitanya Baru,
Naren Chittar,
Xinyong Dong,
Michael Genesereth,
James Hendler,
Aditya Kalyanpur,
Douglas B. Lenat,
Juan F. Sequeda,
Denny Vrandečić,
Kuansan Wang
Publication year - 2022
Publication title -
the ai magazine/ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v43i1.19119
Subject(s) - relation (database) , context (archaeology) , abstraction , computer science , knowledge graph , data science , knowledge management , world wide web , artificial intelligence , epistemology , history , data mining , archaeology , philosophy
Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.