Semantically Classifying Sets Of Data Elements

A method, comprising:

• receiving a set of data elements corresponding to a data column to be semantically classified;
• applying a machine learning model to the set of data elements to predict a set of candidate semantic types for the set of data elements, wherein the set of candidate semantic types comprises, for each data element of the set of data elements, at least one candidate semantic type corresponding to the data element;
• selecting a particular semantic type from the set of candidate semantic types based at least in part on a semantic fit score corresponding to the particular semantic type predicted by the machine learning model;
• presenting the particular semantic type as a recommended semantic classification for the data column;
• wherein the method is performed by at least one device including a hardware processor.

A system comprising:

• at least one hardware processor;
• the system being configured to execute operations, using the at least one hardware processor, the operations comprising:
• receiving a set of data elements corresponding to a data column to be semantically classified;
• applying a machine learning model to the set of data elements to predict a set of candidate semantic types for the set of data elements, wherein the set of candidate semantic types comprises, for each data element of the set of data elements, at least one candidate semantic type corresponding to the data element;
• selecting a particular semantic type from the set of candidate semantic types based at least in part on a semantic fit score corresponding to the particular semantic type predicted by the machine learning model;
• presenting the particular semantic type as a recommended semantic classification for the data column.