US 2025/0148353 A1Published application

Method, System, and Computer Program Product for Machine Learning Using Decoupled Knowledge Graphs

AssigneeVisa International Service Association

InventorsXi Kan, Juncheng Ding, Dan Wang

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Abstract

Described are a method, system, and computer program product for machine learning using decoupled knowledge graphs. The method includes generating a graph including nodes connected by edges based on data of entities in a network. Generating the graph includes generating entity nodes, determining a distribution of values for an attribute of the entities, generating a lower attribute node associated with a lower subset of values for the attribute, generating a higher attribute node associated with a higher subset of values for the attribute, and generating edges connecting the nodes. The method also includes initializing node embeddings, and generating representations of the nodes by repeating, until convergence, updating the embeddings of the entity nodes while holding other embeddings static, and updating the embeddings of the non-entity nodes while holding other embeddings static. The method further includes executing a machine learning model using the representations.

Claims20

Dependent claims

A method comprising:

generating, with at least one processor, a knowledge graph comprising a plurality of nodes connected by a plurality of edges, based on transaction data of a plurality of entities in a network, wherein generating the knowledge graph comprises:
generating a plurality of entity nodes of the plurality of nodes, each entity node of the plurality of entity nodes associated with an entity of the plurality of entities;
determining a distribution of values for at least one attribute of the plurality of entities in the network, based on the transaction data;
generating at least one lower attribute node of the plurality of nodes associated with a lower subset of values for the at least one attribute, based on the distribution of values;
generating at least one higher attribute node of the plurality of nodes associated with a higher subset of values for the at least one attribute, based on the distribution of values;
and
generating the plurality of edges, each edge of the plurality of edges connecting two nodes of the plurality of nodes and being associated with a relationship between the two nodes;
initializing, with at least one processor, embeddings associated with the plurality of nodes with random values;
generating, with at least one processor, learned representations of the plurality of nodes by repeatedly updating the embeddings associated with the plurality of nodes, wherein repeatedly updating the embeddings comprises, until convergence of the embeddings on the learned representations:
updating the embeddings of the plurality of entity nodes while holding the embeddings of a plurality of non-entity nodes of the plurality of nodes static, the plurality of non-entity nodes comprising a set of the plurality of nodes exclusive of the plurality of entity nodes;
and
updating the embeddings of the plurality of non-entity nodes while holding the embeddings of the plurality of entity nodes static;
and
executing, with at least one processor, at least one classification machine learning model using the learned representations of the plurality of nodes.

11 more elements

A system comprising at least one processor programmed or configured to:

generate a knowledge graph comprising a plurality of nodes connected by a plurality of edges, based on transaction data of a plurality of entities in a network, wherein, when generating the knowledge graph, the at least one processor is programmed or configured to:
generate a plurality of entity nodes of the plurality of nodes, each entity node of the plurality of entity nodes associated with an entity of the plurality of entities;
determine a distribution of values for at least one attribute of the plurality of entities in the network, based on the transaction data;
generate at least one lower attribute node of the plurality of nodes associated with a lower subset of values for the at least one attribute, based on the distribution of values;
generate at least one higher attribute node of the plurality of nodes associated with a higher subset of values for the at least one attribute, based on the distribution of values;
and
generate the plurality of edges, each edge of the plurality of edges connecting two nodes of the plurality of nodes and being associated with a relationship between the two nodes;
initialize embeddings associated with the plurality of nodes with random values;
generate learned representations of the plurality of nodes by repeatedly updating the embeddings associated with the plurality of nodes, wherein, when repeatedly updating the embeddings, the at least one processor is programmed or configured to, until convergence of the embeddings on the learned representations:
update the embeddings of the plurality of entity nodes while holding the embeddings of a plurality of non-entity nodes of the plurality of nodes static, the plurality of non-entity nodes comprising a set of the plurality of nodes exclusive of the plurality of entity nodes;
and
update the embeddings of the plurality of non-entity nodes while holding the embeddings of the plurality of entity nodes static;
and
execute at least one classification machine learning model using the learned representations of the plurality of nodes.

11 more elements

A computer program product comprising at least one non-transitory computer-readable medium comprising one or more instructions that, when executed by at least one processor, cause the at least one processor to:

generate a knowledge graph comprising a plurality of nodes connected by a plurality of edges, based on transaction data of a plurality of entities in a network, wherein the one or more instructions that cause the at least one processor to generate the knowledge graph cause the at least one processor to:
generate a plurality of entity nodes of the plurality of nodes, each entity node of the plurality of entity nodes associated with an entity of the plurality of entities;
determine a distribution of values for at least one attribute of the plurality of entities in the network, based on the transaction data;
generate at least one lower attribute node of the plurality of nodes associated with a lower subset of values for the at least one attribute, based on the distribution of values;
generate at least one higher attribute node of the plurality of nodes associated with a higher subset of values for the at least one attribute, based on the distribution of values;
and
generate the plurality of edges, each edge of the plurality of edges connecting two nodes of the plurality of nodes and being associated with a relationship between the two nodes;
initialize embeddings associated with the plurality of nodes with random values;
generate learned representations of the plurality of nodes by repeatedly updating the embeddings associated with the plurality of nodes, wherein the one or more instructions that cause the at least one processor to repeatedly update the embeddings cause the at least one processor to, until convergence of the embeddings on the learned representations:
update the embeddings of the plurality of entity nodes while holding the embeddings of a plurality of non-entity nodes of the plurality of nodes static, the plurality of non-entity nodes comprising a set of the plurality of nodes exclusive of the plurality of entity nodes;
and
update the embeddings of the plurality of non-entity nodes while holding the embeddings of the plurality of entity nodes static;
and
execute at least one classification machine learning model using the learned representations of the plurality of nodes.

11 more elements

Dependencies are inferred from the claim text.