Representation learning with side information

A system comprising:

• a relational similarity determination engine stored in memory and configured to:
• receive as a first input a dataset defining entities and including co-occurrence data, the co-occurrence data including co-occurrence relations for pairs of the entities;
• and
• receive as a second input side information defining explicit relations between the pairs of the entities, the side information including an explicitly-provided relation type identifier that identifies a relationship type for of the pairs of the entities in the dataset;
• and
• jointly model the co-occurrence relations and the explicit relations for the entities to compute similarity metrics for each different pair of the entities within the dataset, the similarity metrics for a given pair of the entities including:
• a first probability distribution representing a relational similarity between two entities of the given pair based on the co-occurrence relations for the two entities within the dataset:
• a second probability distribution representing a second relational similarity between the two entities of the given pair based on the explicitly-provided relation type identifier for the given pair;
• and
• a joint probability distribution that is based on the first probability distribution and the second probability distribution;
• identify, based on the similarity metrics, a most similar replacement entity from the dataset for each of the entities within the dataset;
• and
• for a select entity received as an input, output the most similar replacement entity.

One or more non-transitory computer-readable storage media encoding processor-executable instructions for executing a computer process, the computer process comprising:

• training a machine learning (ML) model with inputs including:
• a dataset defining entities and including co-occurrence data, the co-occurrence data including co-occurrence relations for pairs of the entities;
• and
• side information defining explicit relations between the pairs of the entities, the side information including an explicitly-provided relation type identifier that identifies a relationship type for each of the pairs of the entities in the dataset;
• and
• jointly modeling, with the ML model, the co-occurrence relations and the explicit relations for the entities to compute similarity metrics for each different pair of the entities within the dataset, the similarity metrics for a given pair of the entities including:
• a first probability distribution representing a relational similarity between two entities of the given pair based on the co-occurrence relations for the two entities within the dataset;
• a second probability distribution representing a second relational similarity between the two entities of the given pair based on the explicitly-provided relation type identifier for the given pair;
• and
• a joint probability distribution that is based on the first probability distribution and the second probability distribution;
• based on the joint probability distribution, identifying a most similar replacement entity from the dataset for each of the entities within the dataset;
• and
• for a select entity received as an input, outputting the most similar replacement entity.