Knowledge graphs: Introduction, history, and perspectives

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Chaudhri, V. K.
Baru, C.
Chittar, N.
Dong, X. L.
Genesereth, M.
Hendler, James A.
Kalyanpur, A.
Lenat, D.
Sequeda, Juan
Vrandečić, D.
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Knowledge graphs
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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.
The term knowledge graph (KG) has gained several different meanings across a range of usage scenarios. This paper focuses on the use of KGs in the context of two important current trends: the desire and need to harness the large and diverse data that are now available and the advent of new machine learning capabilities for extracting meaning from unstructured text and images. It provides the authors’ perspective on this area and tracks recent efforts in the NSF Convergence Accelerator Track A on Open Knowledge Network (OKN), where the first author was a participant in one of the projects (Baru el al. 2022). All coauthors were speakers in a graduate seminar on KGs at Stanford University, coorganized by the first author, which featured presentations by over 50 speakers1. This article strives to provide a synthesis of those diverse perspectives—rather than being an exhaustive survey of the topic area.
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Chaudhri, V. K., C. Baru, N. Chittar, X. L. Dong, M. Genesereth, J. Hendler, A. Kalyanpur, D. Lenat, J. Sequeda, D. Vrandečić, and K. Wang 2022. “Knowledge graphs:Introduction, history, and perspectives.” AI Magazine 43: 17–29.
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