Techniques for similarity analysis and data enrichment using knowledge sources

A data enrichment system comprising:

• one or more input data sources configured to store one or more reference data sets;
• and a cloud computing infrastructure system comprising:
• an ingest engine that extracts a first data set from the one or more reference data sets stored in the one or more input data sources;
• an input unit configured to receive a second data set from a user;
• a similarity metric module, comprising a processor and a memory, configured to:
• calculate a similarity metric value between a second subset of data of the second data set and a first subset of data of the first data set, wherein calculating the similarity metric value comprises:
• determining a ratio of a size of an intersection between the second data set and one or more categories of the first data set and a size of a union of data in the second data set and the one or more categories of the first data set;
• and
• in response to the similarity metric value being a predetermined ratio, determining a category name that identifies a category type of the first subset of data of the first data set extracted from the one or more reference data sets stored in the one or more input data sources, wherein the category type comprises a knowledge domain for the first subset of data extracted from the one or more reference data sets;
• and
• a recommendation engine configured to obtain, from the similarity metric module, the determined category name of the first subset of data of the first data set and recommending the determined category name to the user on a user interface for the second subset of data of the second data set.

A non-transitory computer-readable medium comprising instructions which, when executed by one or more processors, causes the one or more processors to:

• store, by one or more input data sources, one or more reference data sets;
• extract, by an ingest engine of a cloud computing infrastructure system, a first data set from the one or more reference data sets stored in the one or more input data sources;
• receive, by an input unit, a second data set from a user;
• calculate, by a similarity metric module, a similarity metric value between a second subset of data of the second data set and a first subset of data of the first data set, wherein calculating the similarity metric value comprises:
• determining a ratio of a size of an intersection between the second data set and one or more categories of the first data set and a size of a union of data in the second data set and the one or more categories of the first data set;
• in response to the similarity metric value being a predetermined ratio, determine a category name that identifies a category type of the first subset of data of the first data set extracted from the one or more reference data sets stored in the one or more input data sources, wherein the category type comprises a knowledge domain for the first subset of data extracted from the one or more reference data sets;
• obtain, by a recommendation engine from the similarity metric module, the determined category name of the first subset of data of the first data set;
• and
• recommend, by the recommendation engine, the determined category name to the user on a user interface for the second subset of data of the second data set.