Partitioning-based scalable weighted aggregation composition for knowledge graph embedding

A method, comprising:

• accessing a knowledge graph comprising a plurality of nodes, each node being connected to at least one other node via one or more edges, wherein a given edge represents a relationship between a pair of nodes;
• partitioning the knowledge graph into a plurality of partitions based on edge densities between nodes of the knowledge graph;
• performing partition-wise encoding using compositional message passing between nodes that enables learning from neighboring nodes in each partition;
• generating an embedding for each node and each relation type in each partition based on the partition-wise encoding using compositional message passing, wherein the embedding for each node is based on learned knowledge from neighboring nodes;
• concatenating the generated embeddings from the plurality of partitions;
• and
• training a global neural network for a downstream prediction task based on the concatenated embeddings using one or more weight matrices.

A non-transitory computer readable medium storing instructions that, when executed by a processor, programs the processor to:

• access a knowledge graph comprising a plurality of nodes, each node being connected to at least one other node via one or more edges, wherein a given edge represents a relationship between a pair of nodes;
• partition the knowledge graph into a plurality of partitions based on edge densities between nodes of the knowledge graph;
• perform partition-wise encoding using compositional message passing between nodes that enables learning from neighboring nodes in each partition;
• generate an embedding for each node and each relation type in each partition based on the partition-wise encoding using compositional message passing, wherein the embedding for each node is based on learned knowledge from neighboring nodes;
• concatenate the generated embeddings from the plurality of partitions;
• and
• train a neural network for a downstream prediction task based on the concatenated embeddings using one or more weight matrices.