Method for inductive knowledge graph embedding using relation graphs and system thereof

A non-transitory computer-readable recording medium storing instructions that, when executed by a processor, cause the processor to execute an inductive knowledge graph embedding method performed by a knowledge graph embedding system, the inductive knowledge graph embedding method comprising:

• training a graph neural network-based knowledge graph embedding model using a knowledge graph and a relation graph generated from the knowledge graph;
• and
• performing link prediction for the knowledge graph that includes a new relation and a new entity through the trained graph neural network-based knowledge graph embedding model,
• the training comprises training the graph neural network-based knowledge graph embedding model by repartitioning the knowledge graph into a fact set and a training set at regular intervals through a dynamic split technique, and
• the graph neural network-based knowledge graph embedding model includes a graph neural network on the relation graph to update representation vectors that reflect relationship information between relations using the structure of the relation graph and a graph neural network on the knowledge graph to update representation vectors that reflect connectivity between relations and entities within the knowledge graph using the structure of the knowledge graph.

A knowledge graph embedding system comprising:

• a training unit configured to train a graph neural network-based knowledge graph embedding model using a knowledge graph and a relation graph generated from the knowledge graph;
• and
• an inference unit configured to perform link prediction for the knowledge graph that includes a new relation and a new entity through the trained graph neural network-based knowledge graph embedding model,
• wherein the training unit is configured to train the graph neural network-based knowledge graph embedding model by repartitioning a knowledge graph into a fact set and a training set at regular intervals through a dynamic split technique, and
• the graph neural network-based knowledge graph embedding model includes a graph neural network on the relation graph to update representation vectors that reflect relationship information between relations using the structure of the relation graph and a graph neural network on the knowledge graph to update representation vectors that reflect connectivity between relations and entities within the knowledge graph using the structure of the knowledge graph.