Approximate Query Answering over Open Data
Approximate Query Answering over Open Data
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DOI：
10.1145/3597465.3605227
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Mengqi Zhang;Pranay Mundra;Chukwubuikem Chikweze;F. Nargesian;G. Weikum
中科院分区：
文献类型：
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作者：
Mengqi Zhang;Pranay Mundra;Chukwubuikem Chikweze;F. Nargesian;G. Weikum
开放知识，包括开放数据和公开可用的知识库，为数据科学家提供了丰富的分析和查询回答的机会，但由于其数据生态系统的多样性、嘈杂和不完整的性质，也存在很大的障碍。本文提出了通过开放知识 (Quok) 实现近似查询回答的愿景，重点是支持涉及识别相关数据和计算聚合的分析任务。我们定义问题，概述系统架构，并讨论克服开放知识的不确定性和不完整性的挑战和方法。
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.