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Infrastructure for rapid open knowledge network development

Authors :
Cafarella, Michael
Anderson, Michael
Beltagy, Iz
Cattan, Arie
Chasins, Sarah
Dagan, Ido
Downey, Doug
Etzioni, Oren
Feldman, Sergey
Gao, Tian
Hope, Tom
Huang, Kexin
Johnson, Sophie
King, Daniel
Lo, Kyle
Lou, Yuze
Shapiro, Matthew
Shen, Dinghao
Subramanian, Shivashankar
Wang, Lucy Lu
Wang, Yuning
Wang, Yitong
Weld, Daniel S.
Vo-Phamhi, Jenny
Zeng, Anna
Zou, Jiayun
Source :
AI Magazine; March 2022, Vol. 43 Issue: 1 p59-68, 10p
Publication Year :
2022

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 (akaknowledge 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.

Details

Language :
English
ISSN :
07384602 and 23719621
Volume :
43
Issue :
1
Database :
Supplemental Index
Journal :
AI Magazine
Publication Type :
Periodical
Accession number :
ejs59485601
Full Text :
https://doi.org/10.1002/aaai.12038