System and method for linking records using multi-stage machine learning models and contextual data analysis

A system comprising:

• at least one processor;
• and
• at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
• convert a set of genealogical records into embeddings using a first machine learning model;
• compare, among the embeddings of the set of genealogical records, embeddings of target genealogical records to an embedding of a source genealogical record in a latent space of the first machine learning model;
• identify a set of candidate genealogical records based on comparing the embeddings of target genealogical records to the embedding of the source genealogical record;
• apply a second machine learning model to generate similarity scores by comparing field similarities between candidate genealogical record and the source genealogical record;
• generate, based on applying a third machine learning model that processes contextual data associated with the set of genealogical records, a match likelihood score that indicates a likelihood of a match between a candidate genealogical record from the set of candidate genealogical records and the source genealogical record;
• and
• determine a link between the source genealogical record and a target genealogical record of the target genealogical records based on the similarity scores and the match likelihood score.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing device to:

• convert a set of genealogical records into embeddings using a first machine learning model;
• compare, among the embeddings of the set of genealogical records, embeddings of target genealogical records to an embedding of a source genealogical record in a latent space of the first machine learning model;
• identify a set of candidate genealogical records based on comparing the embeddings of target genealogical records to the embedding of the source genealogical record;
• apply a second machine learning model to generate similarity scores by comparing field similarities between the set of candidate genealogical records and the source genealogical record;
• generate, based on applying a third machine learning model that processes contextual data associated with the set of candidate genealogical records, a match likelihood score that indicates a likelihood of a match between a candidate genealogical record from the set of candidate genealogical records and the source genealogical record;
• and
• determine a link between the source genealogical record and a target genealogical record of the target genealogical records based on the similarity scores and the match likelihood score.