Use this component when you wish to match attributes across two schemas or when you wish to generate scripts (from schema matchings) that can be executed to transform data from one format into another.
Wit contains algorithms for string classification and string embeddings using ‘weak’ supervision. To use Wit for schema matching, one has to first learn an embedding of strings into dense N-dimensional vector representations and then align variables whose embedded distributions are “close”.
FlexMatcher: a schema matching package that uses a number of machine learning techniques to train a schema matcher using information from the schema and/or available data. The upcoming version of FlexMatcher would allow users to deploy a wide range of machine learning techniques (from Python’s scikit-learn) to train more efficient models for the schema-matching task.