Accepted Papers

Sherlock: A Deep Learning Approach to Semantic Data Type Detection

Madelon Hulsebos (Massachusetts Institute of Technology);Kevin Hu (Massachusetts Institute of Technology);Michiel Bakker (Massachusetts Institute of Technology);Emanuel Zgraggen (Massachusetts Institute of Technology);Arvind Satyanarayan (Massachusetts Institute of Technology);Tim Kraska (Massachusetts Institute of Technology);Çağatay Demiralp (Massachusetts Institute of Technology);César Hidalgo (Massachusetts Institute of Technology);

Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery. Existing data preparation and analysis systems rely on dictionary lookups and regular expression matching to detect semantic types. However, these matching-based approaches often are not robust to dirty data and only detect a limited number of types. We introduce Sherlock, a multi-input deep neural network for detecting semantic types. We train Sherlock on $686,765$ data columns retrieved from the VizNet corpus by matching $78$ semantic types from DBpedia to column headers. We characterize each matched column with $1,588$ features describing the statistical properties, character distributions, word embeddings, and paragraph vectors of column values. Sherlock achieves a support-weighted F$_1$ score of $0.89$, exceeding that of machine learning baselines, dictionary and regular expression benchmarks, and the consensus of crowdsourced annotations.


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