Semantic data type classification in rectangular datasets

A system comprising:

• a processor;
• and
• a computer-readable storage medium communicatively coupled to the processor and storing program instructions which, when executed by the processor, cause the processor to perform a method comprising:
• receiving one or more rectangular datasets, wherein the one or more rectangular datasets comprise a plurality of columns having a set of known semantic data types;
• extracting a set of features from the plurality of columns, wherein the set of features is used to determine a relationship among each column of the plurality of columns;
• constructing a set of training data based on the extracted set of features;
• training, using the set of training data, a machine learning model to predict a semantic data type of a target column in a rectangular dataset having an unknown semantic data type;
• receiving a first rectangular dataset, wherein the first rectangular dataset contains a first target column having a first unknown semantic data type and a first plurality of other columns containing known semantic data types;
• analyzing, using the machine learning model, a first set of local features associated with the first target column;
• correlating the first set of local features of the first target column with a set of known features of the first plurality of other columns of the first rectangular dataset, wherein at least one known feature includes a name of each column of the first plurality of columns;
• and
• predicting, based on the correlating, a likelihood that the first unknown semantic data type is a specific semantic data type.

A computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:

• receiving one or more rectangular datasets, wherein the one or more rectangular datasets comprise a plurality of columns having a set of known semantic data types;
• extracting a set of features from the plurality of columns, wherein the set of features is used to determine a relationship among each column of the plurality of columns;
• constructing a set of training data based on the extracted set of features;
• training, using the set of training data, a machine learning model to predict a semantic data type of a target column in a rectangular dataset having an unknown semantic data type;
• receiving a first rectangular dataset, wherein the first rectangular dataset contains a first target column having a first unknown semantic data type and a first plurality of other columns containing known semantic data types;
• analyzing, using the machine learning model, a first set of local features associated with the first target column;
• correlating the first set of local features of the first target column with a set of known features of the first plurality of other columns of the first rectangular dataset, wherein at least one known feature includes a name of each column of the first plurality of columns;
• and
• predicting, based on the correlating, a likelihood that the first unknown semantic data type is a specific semantic data type.