We noticed that the people who use the data are usually not the same people who produce the data, and they often don’t know where to find the information about the data they try to use. Since the Schematizer already has the knowledge about all the schemas in the Data Pipeline, it becomes an excellent candidate to store information about the data. Meet our knowledge explorer, Watson. The Schematizer requires schema registrars to include documentation along with their schemas. The documentation then is extracted and stored in the Schematizer. To make the schema information and data documentation in the Schematizer accessible to all the teams at Yelp, we created Watson, a webapp that users across the company can use to explore this data. Watson is a visual frontend for the Schematizer and retrieves its information through a set of RESTful APIs exposed by the Schematizer.

At Yelp, SQLAlchemy is used to describe all models in our databases.

The richnessand subtlety of natural language, however, makes RTEhighly challenging. To facilitate the development of strongRTE systems, increasingly larger datasets have been pro-posed, ranging in size from 100s to over 500,000 annotatedpremise-hypothesis pairs. Datasets such as RTE-n (Dagan,Glickman, and Magnini 2005), SICK (Marelli et al. 2014),and SNLI (Bowman et al. 2015) have played an importantrole in advancing the field.