Open AccessInfrastructure for Rapid Open Knowledge Network Development
Author(s) -
Michael Cafarella,
Michael Anderson,
Iz Beltagy,
Arie Cattan,
Sarah Chasins,
Ido Dagan,
Doug Downey,
Oren Etzioni,
Sergey Feldman,
Ganghong Tian,
Tom Hope,
Kexin Huang,
Sophie Johnson,
Daniel King,
Kyle Lo,
Yuze Lou,
Matthew D. Shapiro,
Dinghao Shen,
Shivashankar Subramanian,
Lucy Wang,
Yuning Wang,
Yitong Wang,
Daniel S. Weld,
Jenny VoPhamhi,
Anna Zeng,
Jiayun Zou
Publication year - 2022
Publication title -
the ai magazine/ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v43i1.19126
Subject(s) - computer science , knowledge graph , aka , data science , world wide web , information retrieval , library science
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.