Entity standardization for application modernization

A system, comprising:

• One or more processors configured to initiate operations including:
• extracting a mention of a computer application component from a free-form text, wherein the free-form text is a textual description of a technology stack of the computer application;
• encoding the mention with an embedding space encoder, wherein the encoding creates an encoded representation of the mention in a multi-dimensional embedding space, and wherein the embedding space encoder implements a machine learning model trained using contrastive learning;
• mapping the encoded representation of the mention to an encoded representation of an entity in the multi-dimensional embedding space, wherein the entity is extracted from a knowledge base of computer components;
• and
• outputting the entity whose encoded representation maps to the encoded representation of the mention.

A computer program product, the computer program product comprising:

• one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions executable by a processor to cause the processor to initiate operations including:
• extracting a mention of a computer application component from a free-form text, wherein the free-form text is a textual description of a technology stack of the computer application;
• encoding the mention with an embedding space encoder, wherein the encoding creates an encoded representation of the mention in a multi-dimensional embedding space, and wherein the embedding space encoder implements a machine learning model trained using contrastive learning;
• mapping the encoded representation of the mention to an encoded representation of an entity in the multi-dimensional embedding space, wherein the entity is extracted from a knowledge base of computer components;
• and
• outputting the entity whose encoded representation maps to the encoded representation of the mention.

A method, comprising:

• generating, with a processor, a plurality of triplet examples to train a contrastive learning model;
• iteratively adjusting, by the processor, parameters of the contrastive learning model during an initial sequence of training epochs using batch-all mining with the triplet examples;
• and
• further adjusting, by the processor, parameters of the contrastive learning model during a subsequent sequence of training epochs using batch-hard mining as the parameters converge to a final set of values.