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Title: Approximate Query Answering over Open Data
Open knowledge, including open data and publicly available knowledge bases, offers a rich opportunity for data scientists for analysis and query answering, but comes with big obstacles due to the diverse, noisy, and incomplete nature of its data eco-system. This paper proposes a vision for enabling approximate QUery answering over Open Knowledge (Quok), with a focus on supporting analytic tasks that involve identifying relevant data and computing aggregations. We define the problem, outline a system architecture, and discuss challenges and approaches to taming the uncertainty and incompleteness of open knowledge.  more » « less
Award ID(s):
2107050
PAR ID:
10465859
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
HILDA'23: The SIGMOD 2023 Workshop on Human-in-the-Loop Data Analytics
Page Range / eLocation ID:
1 to 3
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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