Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery

Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery

© 2018 IEEE. Employees that spend more time finding relevant data than analyzing it suffer from a data discovery problem. The large volume of data in enterprises, and sometimes the lack of knowledge of the schemas aggravates this problem. Similar to how we navigate the Web, we propose to identify se...

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Main Authors: Castro Fernandez, Raul (Author), Mansour, Essam (Author), Qahtan, Abdulhakim A. (Author), Elmagarmid, Ahmed (Author), Ilyas, Ihab (Author), Madden, Samuel (Author), Ouzzani, Mourad (Author), Stonebraker, Michael (Author), Tang, Nan (Author)
Format: Article
Language:English
Published: IEEE, 2021-11-09T12:48:12Z.
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