Abstract: Embodiments are directed towards managing data. A data engine provides a data model that may include a plurality of concepts and a plurality of relations between the concepts. The data engine associates a propagation weight with each relation based on characteristics of the concepts. A feature engine associates an initial impetus value with a pivot concept. The feature engine employs the pivot concept as a start point to recursively traverse the data model. The feature engine allocates a portion of the impetus value to concepts that may be on a direct path of the traversal based on the propagation weight associated with each relation of the concepts. The feature engine may identify feature concepts based on a value of a portion of the impetus value that exceeds a threshold.

Abstract: A data model is traversed to determine concept characteristics associated with concepts that may be associated with entities. Associated documents may be evaluated to identify document characteristics associated with the entities. Entity models may be trained based on the concept characteristics and the document characteristics with each entity model being associated with a confidence value. Results for one or more queries based on the documents and the entity models may be provided. The results may reference the documents that may be associated with the entities. Some entity models may produce results that have a confidence value below a threshold value. Accordingly, the entity models that provide low confidence results may be re-trained.