Systems and methods for generalized structured data discovery utilizing contextual metadata disambiguation via machine learning techniques

A system for generalized structured data discovery utilizing contextual metadata disambiguation via machine learning techniques, comprising:

• a plurality of data sources of physical application metadata for an attribute, a database object, or a database;
• at least one organizational database comprising reference data comprising a plurality of tokens and their associated abbreviations and acronyms;
• and
• a language processing engine comprising at least one computer processor in communication with the plurality of data sources;
• wherein:
• the language processing engine receives the physical application metadata from the data sources;
• the language processing engine receives the reference data from the organizational database;
• the language processing engine parses the physical application metadata into a plurality of application tokens comprising known application tokens and unknown application tokens;
• the language processing engine identifies unknown application tokens by comparing the parsed application tokens to a corpus;
• the language processing engine performs probabilistic parsing on the unknown application tokens using the reference data;
• the language processing engine performs bi-directional encoding on polysemous unknown application tokens to expand the polysemous tokens to relevant expressions using the reference data;
• the language processing engine applies language tokens to the relevant expressions in the expanded polysemous tokens to disambiguate the relevant expressions;
• and
• the language processing engine outputs a mapping of the physical application metadata to enhanced physical application metadata, wherein the enhanced physical application metadata comprises an expression for the physical application metadata in a supported language.