Infrastructure for rapid open knowledge network development
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 01, 2022
- Source ID
- 10.1002/aaai.12038
Entities
People
- Anna Zeng
- Arie Cattan
- Daniel S. King
- Daniel S. Weld
- Dinghao Shen
- Doug Downey
- Ido Dagan
- Iz Beltagy
- Jenny Vo‐phamhi
- Jiayun Zou
- Kexin Huang
- Kyle Lo
- Lucy Lu Wang
- Matthew Shapiro
- Michael Anderson
- Michael Cafarella
- Oren Etzioni
- Sarah Chasins
- Sergey Feldman
- Shivashankar Subramanian
- Sophie Johnson
- Tian Gao
- Tom Hope
- Yitong Wang
- Yuning Wang
- Yuze Lou
Organizations
- Allen Institute for Artificial Intelligence
- Bar-Ilan University
- Massachusetts Institute of Technology
- National Science Foundation
- Office of Naval Research
- University of California
- University of Michigan