Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery

• 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.
• Subjects: Article
• Online Access: Get fulltext

 © 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 semantic links that assist analysts in their discovery tasks. These links relate tables to each other, to facilitate navigating the schemas. They also relate data to external data sources, such as ontologies and dictionaries, to help explain the schema meaning. We materialize the links in an enterprise knowledge graph, where they become available to analysts. The main challenge is how to find pairs of objects that are semantically related. We propose SEMPROP, a DAG of different components that find links based on syntactic and semantic similarities. SEMPROP is commanded by a semantic matcher which leverages word embeddings to find objects that are semantically related. We introduce coherent group, a technique to combine word embeddings that works better than other state of the art combination alternatives. We implement SEMPROP as part of Aurum, a data discovery system we are building, and conduct user studies, real deployments and a quantitative evaluation to understand the benefits of links for data discovery tasks, as well as the benefits of SEMPROP and coherent groups to find those links.