Knowledge Graphs: Introduction, History and, Perspectives

Authors

  • 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 data.world
  • Denny Vrandečić Wikimedia Foundation
  • Kuansan Wang Microsoft

DOI:

https://doi.org/10.1609/aimag.v43i1.19119

Abstract

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.

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Published

2022-03-31

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. https://doi.org/10.1609/aimag.v43i1.19119

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Section

Special Topic Articles