Infrastructure for rapid open knowledge network development

Michael Cafarella*, Michael Anderson, Iz Beltagy, Arie Cattan, Sarah Chasins, Ido Dagan, Doug Downey, Oren Etzioni, Sergey Feldman, Tian Gao, Tom Hope, Kexin Huang, Sophie Johnson, Daniel King, Kyle Lo, Yuze Lou, Matthew Shapiro, Dinghao Shen, Shivashankar Subramanian, Lucy Lu WangYuning Wang, Yitong Wang, Daniel S. Weld, Jenny Vo-Phamhi, Anna Zeng, Jiayun Zou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageAmerican English
Pages (from-to)59-68
Number of pages10
JournalAI Magazine
Issue number1
StatePublished - 1 Mar 2022
Externally publishedYes

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