English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

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

Sherlock: A Deep Learning Approach to Semantic Data Type Detection