Subgraph guided knowledge graph question generation

A computer program product for subgraph guided knowledge graph question generation, the computer program product comprising:

• one or more computer readable storage media, and program instructions stored on the one or more computer readable storage media, the program instructions comprising:
• program instructions to input a knowledge graph subgraph and a target answer into an encoding module, wherein the knowledge graph subgraph is a collection of entities and predicates relating to a domain and represented as nodes for the entities and edges for the predicates;
• program instructions to produce, by the encoding module, embeddings related to the nodes and the edges, wherein each of the nodes and the edges in the subgraph is an embedding represented as an initial vector in an embedding space;
• program instructions to indicate the embeddings associated with the target answer;
• program instructions to apply a graph neural network (GNN) encoder computation in an iterative manner to the embeddings, with updated embeddings produced by the GNN encoder acting as initial values that are applied to the GNN encoder for a next iteration, until final state embeddings are produced;
• program instructions to generate a graph-level embedding based on the final state embeddings;
• and
• program instructions to compute, by a recurrent neural network (RNN) decoder, a question relating to the target answer and the knowledge graph subgraph using the graph-level embedding.

A system for subgraph guided knowledge graph question generation, the system comprising:

• a memory;
• a data processing component;
• local data storage having stored thereon computer executable program code;
• an encoding module configured to produce embeddings relating to nodes and edges from a knowledge graph subgraph and a target answer, wherein the knowledge graph subgraph is a collection of entities and predicates relating to a domain and represented as the nodes for the entities and the edges for the predicates;
• a graph neural network (GNN) encoder configured to apply a computation in an iterative manner to the embeddings, with updated embeddings produced by the GNN encoder acting as initial values that are applied to the GNN encoder for a next iteration, until final state embeddings are produced;
• a graph line embedder configured to generate a graph-level embedding based on the final state embeddings;
• and
• a recurrent neural network configured to compute a question relating to the target answer and the knowledge graph subgraph using the graph-level embedding.