3. The system of claim 1 , further comprising a machine learned classifier model that is trained using the feature set to output the relevance scores for the given entity and the associated attributes.
1. A system, comprising: at least one knowledge base of entities, entity types, and entity attributes; a features component configured to compute a plurality of intermediate features of the entities and attributes for each entity, and aggregate the intermediate features to output a final feature set of features having an entity-attribute tuple corresponding to the respective plurality of computed intermediate features; a relevance component configured to generate a relevance score for a given entity and associated attributes based on the feature set, wherein the relevance of each attribute of the entity is based on a set of judgments from human judges; a ranking component configured to rank the attributes of the given entity based on the relevance scores; and a microprocessor that executes computer-executable instructions associated with at least one of the features component, relevance component, or the ranking component.