Deep Data Integration

W. Tan
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引用次数: 3

Abstract

We are witnessing the widespread adoption of deep learning techniques as avant-garde solutions to different computational problems in recent years. In data integration, the use of deep learning techniques has helped establish several state-of-the-art results in long standing problems, including information extraction, entity matching, data cleaning, and table understanding. In this talk, I will reflect on the strengths of deep learning and how that has helped move the needle in data integration. I will also discuss a few challenges associated with solutions based on deep learning techniques and describe some opportunities for the data management community.
深度数据集成
近年来,我们见证了深度学习技术作为各种计算问题的前卫解决方案的广泛采用。在数据集成中,深度学习技术的使用有助于在长期存在的问题上建立几个最先进的结果,包括信息提取、实体匹配、数据清理和表理解。在这次演讲中,我将反思深度学习的优势,以及它如何帮助推动数据集成。我还将讨论与基于深度学习技术的解决方案相关的一些挑战,并描述数据管理社区的一些机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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