Infrastructure for Rapid Open Knowledge Network Development

Authors

  • Michael Cafarella MIT CSAIL
  • Michael Anderson University of Michigan
  • Iz Beltagy Allen Institute for Artificial Intelligence
  • Arie Cattan Allen Institute for Artificial Intelligence
  • Sarah Chasins University of California, Berkeley
  • Ido Dagan Allen Institute for Artificial Intelligence
  • Doug Downey Allen Institute for Artificial Intelligence
  • Oren Etzioni Allen Institute for Artificial Intelligence
  • Sergey Feldman Allen Institute for Artificial Intelligence
  • Tian Gao University of Michigan
  • Tom Hope Allen Institute for Artificial Intelligence
  • Kexin Huang University of Michigan
  • Sophie Johnson Allen Institute for Artificial Intelligence
  • Daniel King Allen Institute for Artificial Intelligence
  • Kyle Lo Allen Institute for Artificial Intelligence
  • Yuze Lou University of Michigan
  • Matthew Shapiro University of Michigan
  • Dinghao Shen University of Michigan
  • Shivashankar Subramanian Allen Institute for Artificial Intelligence
  • Lucy Lu Wang Allen Institute for Artificial Intelligence
  • Yuning Wang University of Michigan
  • Yitong Wang University of Michigan
  • Daniel Weld Allen Institute for Artificial Intelligence
  • Jenny Vo-Phamhi University of Michigan
  • Anna Zeng MIT CSAIL
  • Jiayun Zou University of Michigan

DOI:

https://doi.org/10.1609/aimag.v43i1.19126

Abstract

The past decade has witnessed a growth in the use of knowledge graph technologies for advanced data search, data integration, and query-answering applications. The leading example of a public, general-purpose open knowledge network (aka knowledge graph) is Wikidata, which has demonstrated remarkable advances in quality and coverage over this time. Proprietary knowledge graphs drive some of the leading applications of the day including, for example, Google Search, Alexa, Siri, and Cortana. Open Knowledge Networks are exciting: they promise the power of structured database-like queries with the potential for the wide coverage that is today only provided by the Web. With the current state of the art, building, using, and scaling large knowledge networks can still be frustratingly slow. This article describes a National Science Foundation Convergence Accelerator project to build a set of Knowledge Network Programming Infrastructure systems to address this issue.

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Published

2022-03-31

How to Cite

Cafarella, M., Anderson , M. ., Beltagy, I. ., Cattan, A. ., Chasins, S. ., Dagan, I. ., Downey, D. ., Etzioni, O. ., Feldman, S. ., Gao, T. ., Hope, T. ., Huang, K. ., Johnson, S. ., King, D. ., Lo, K. ., Lou, Y. ., Shapiro, M. ., Shen, D. ., Subramanian, S. ., Wang, L. ., Wang, Y. ., Wang, Y. ., Weld, D. ., Vo-Phamhi, J. ., Zeng, A. ., & Zou, J. . (2022). Infrastructure for Rapid Open Knowledge Network Development. AI Magazine, 43(1), 59-68. https://doi.org/10.1609/aimag.v43i1.19126

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Special Topic Articles