28. A non-transitory computer-readable storage medium including instructions that, when executed by processing resources including at least one processor and a memory, are configured to at least: receive, over an input interface, documents comprising data entries, at least some of the data entries having associated features represented directly in the documents; inspect documents received via the input interface to identify, as heterogeneous input data, data entries and associated features located in the inspected documents; segment the heterogeneous input data into different, respectively homogeneous processing groups, each processing group having an associated level of information uncertainty; for each different processing group, starting with the processing group associated with a lowest level of information uncertainty and moving upwardly: (a) identify one or more corresponding model stacks from a model registry to be executed on the respective processing group, the model registry storing a plurality of different model stacks, each model stack including at least one classification model and at least one different and independent confidence model; (b) execute each identified model stack on the respective processing group to arrive at a classification result and a confidence level for each data entry in the respective processing group using the classification and confidence models in the respective model stack, wherein classification results map features from the data entries to predefined concepts associated with the classification models; (c) adaptively ensemble results from the execution of each identified model stack, using the classification results and the confidence levels, along with a processing group specific ensembler, to group the data entries in the processing group into one of first and second result type groups, the first result type group corresponding to a confirmed classification and the second result type group corresponding to an unconfirmed classification; and (d) move each data entry in the first result type group to a final result set; and (e) for the second result type group: determine, for each data entry in the second result type group, the processing group from among those processing groups not yet processed that is most closely related to it; and move each data entry, and the associated classification results and confidence levels, in the second result type group to the corresponding determined most closely related processing group; once all of the different processing groups have been processed in accordance with (a) through (e), treat as unclassified any data entries remaining in the second result type group; store each data entry in the final result set, with or without additional processing, to a data warehouse that is configured to store curated and classified data elements, in accordance with the corresponding arrived at classification result; and reference records in the data warehouse in response to queries from a computer terminal.
29. The non-transitory computer-readable storage medium of claim 28 , wherein at least one of the model stacks includes a vector space model configured to output classification results and a family of Bayesian generalized linear models configured to assign confidence scores to classification results output from the vector space model.