Abstract: A computer-implemented method of querying a graph to assess relationships amongst graph nodes comprises determining a query node on the graph, identifying one or more target nodes on the graph in relation to the query node based on a set of connectivity patterns; generating graph-based statistics for each target node of the one or more target nodes, wherein the graph-based statistics are extracted for subgraphs associated with each target node and the query node; and assessing the graph-based statistics of each target node to determine predicted relationships between the one or more target nodes and the query node.

Abstract: Method(s), apparatus and system(s) are provided for entity type identification and/or disambiguation of entities within a corpus of text the method including: receiving one or more entity results, each entity result comprising data representative of an identified entity and a location of the identified entity within the corpus of text; identifying an entity type for each entity of the received entity results by inputting text associated with the location of said each entity in the corpus of text to a trained entity type (ET) model configured for predicting or extracting an entity type of said each entity from the corpus of text; and outputting data representative of the identified entity type of each entity in the received entity results.

Abstract: Methods and apparatus are provided for generating a graph neural network (GNN) model based on an entity-entity graph. The entity-entity graph comprising a plurality of entity nodes in which each entity node is connected to one or more entity nodes of the plurality of entity nodes by one or more corresponding relationship edges. The method comprising: generating an embedding based on data representative of the entity-entity graph for the GNN model, wherein the embedding comprises an attention weight assigned to each relationship edge of the entity-entity graph; and updating weights of the GNN model including the attention weights by minimising a loss function associated with at least the embedding; wherein the attention weights indicate the relevancy of each relationship edge between entity nodes of the entity-entity graph. The entity-entity graph may be filtered based on the attention weights of a trained GNN model.