Conference PaperApproximate Query Answering over Open DataZhang, Mengqi; Mundra, Pranay; Chikweze, Chukwubuikem; Nargesian, Fatemeh; Weikum, GerhardOpen 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.2023-06-1810465859HILDA'23: The SIGMOD 2023 Workshop on Human-in-the-Loop Data Analytics1 to 3https://doi.org/10.1145/3597465.36052272107050National Science Foundation