Sherlock: A deep learning approach to semantic data type detection
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019•dl.acm.org
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 …
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 …
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 data columns retrieved from the VizNet corpus by matching semantic types from DBpedia to column headers. We characterize each matched column with features describing the statistical properties, character distributions, word embeddings, and paragraph vectors of column values. Sherlock achieves a support-weighted F score of , exceeding that of machine learning baselines, dictionary and regular expression benchmarks, and the consensus of crowdsourced annotations.
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