Abstract: There is provided a method and system for training an embedding model to perform relation predictions in a knowledge hypergraph to output a trained embedding model. A training dataset comprising tuples representing relations between entities in the knowledge hypergraph are received. The embedding model is trained to perform relation predictions for each given tuple from a subset of tuples in the training dataset by generating a respective entity vector for each entity and a respective relation matrix representing relations between the entities. The entity vectors and relation matrix are split into a plurality of windows, and interaction values between elements in each window are calculated. A relation score indicative of the relation in the given tuple being true is calculated. Parameters of the embedding model are updated based on the relation scores for the subset of tuples. The trained embedding model is then output.

Abstract: A graph structure having nodes and edges is represented as an adjacency matrix, and nodes of the graph structure have node features. A computer-implemented method and system for generating a graph structure are provided, the method comprising: generating an adjacency matrix based on a plurality of node features; generating a plurality of noisy node features based on the plurality of node features; generating a plurality of denoised node features using a neural network based on the plurality of noisy node features and the adjacency matrix; and updating the adjacency matrix based on the plurality of denoised node features.