Knowledge Graphs: Introduction, History and, Perspectives


  • Vinay K Chaudhri Stanford University
  • Chaitanya Baru UC San Diego
  • Naren Chittar JPMorgan Chase & Co.
  • Xin Luna Dong Meta AR/VR Assistant
  • Michael Genesereth Stanford University
  • James Hendler Renssalear Polytechnic Institute
  • Aditya Kalyanpur Elemental Cognition
  • Douglas B. Lenat Cycorp
  • Juan Sequeda
  • Denny Vrandečić Wikimedia Foundation
  • Kuansan Wang Microsoft



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.




How to Cite

Chaudhri, V. ., Baru, C. ., Chittar, N. ., Dong , X. ., Genesereth, M. ., Hendler, J. ., Kalyanpur, A. ., Lenat, D. ., Sequeda, J. ., Vrandečić, D. ., & Wang, K. . (2022). Knowledge Graphs: Introduction, History and, Perspectives . AI Magazine, 43(1), 17-29.



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