Certain example embodiments described herein relate to techniques for managing “bad” or “imperfect” data being imported into a database system. That is, certain example embodiments provide a lifecycle technology solution that helps receive data from a variety of different data sources of a variety of known and/or unknown formats, standardize it, fit it to a known taxonomy through model-assisted classification, store it to a database in a manner that is consistent with the taxonomy, and allow it to be queried for a variety of different usages. Some or all of the disclosed technology concerning auto-classification, enrichment, clustering model and model stacks, and/or the like, may be used in these and/or other regards.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data classification system, comprising: an input interface configured to receive documents comprising data entries, at least some of the data entries having associated features represented directly in the documents; a non-transitory computer readable storage medium comprising a data warehouse configured to store curated and classified data elements; a model registry storing at least one model stack, each model stack including at least one classification model and at least one confidence model that is separate from the at least one classification model in the respective model stack, each classification model being configured to generate proposed classifications in accordance with a multi-element taxonomy, with each element in the multi-element taxonomy denoting a respective category, each confidence model being configured to make a trust or do not trust decision relative to a proposed classification generated by the at least one classification model in the associated model stack; and processing resources including at least one processor and a memory, the memory comprising instructions executable to at least: inspect documents received via the input interface to identify data entries and associated features located in the inspected documents; identify one or more model stacks from the model registry for execution on the identified data entries; execute the at least one classification model in the identified one or more model stacks to arrive at proposed classifications for the identified data entries; execute the at least one confidence model in the identified one or more model stacks to arrive at trust or do not trust decisions for each of the arrived at proposed classifications based on a likelihood of category collision occurring when executing the at least one classification model, wherein category collision represents classification uncertainty as between two or more elements in the taxonomy for a given data entry, and wherein trust decisions indicate absences of category collisions; store, to the data warehouse, data entries and their associated proposed classifications, for those proposed classifications where trust decisions have been reached via execution of the at least one confidence model; and reference records in the data warehouse in response to queries from a computer terminal. 2. The system of claim 1, wherein each confidence model is category-specific with respect to the multi-element taxonomy. 3. The system of claim 1, wherein category collisions reflect probabilities of miscategorizations. 4. The system of claim 1, wherein: the model registry stores a plurality of model stacks, at least one of the model stacks including a plurality of classification models and one or more confidence models, and wherein a form of voting is implemented to combine output from the execution of plural classification models in order to generate a single proposed classification therefrom. 5. The system of claim 4, wherein the form of voting is simple majority-rules voting. 6. The system of claim 4, wherein the form of voting is a form of proportional voting. 7. The system of claim 4, wherein the form of voting includes a combination of simple majority-rules voting and a form of proportional voting. 8. The system of claim 1, wherein receivable documents are heterogeneous. 9. The system of claim 8, wherein receivable documents are heterogeneous in terms of different applicable context profiles, and wherein the context profiles represent one or more of known vs. unknown suppliers, language, operation type, and format. 10. The system of claim 1, further comprising: an attribute database configured to store attributes associated with elements in the multi-element taxonomy; wherein the memory comprises further instructions executable to at least: retrieve, for data entries having proposed classifications where trust decisions have been reached, relevant attributes from the attribute database based on granular classifications of the respective data entries to the multi-element taxonomy; and associate the retrieved relevant attributes with the respective data entries for storage to the data warehouse. 11. The system of claim 10, wherein the memory comprises further instructions executable to at least: derive and assign values to at least some of the retrieved relevant attributes by performing unstructured text processing based on (a) a domain of values for the respective retrieved relevant attributes, and (b) text available from the inspected documents. 12. The system of claim 11, wherein the memory comprises further instructions executable to at least: apply a rule-based weighted search on an item universe to retrieve candidate matching records for retrieved relevant attributes lacking derived and assigned values following the unstructured text processing. 13. The system of claim 12, wherein the memory comprises further instructions executable to at least: augment retrieved relevant attributes lacking derived and assigned values following the unstructured text processing with appropriate values determined via match resolution performed in connection with the retrieved candidate matching records. 14. The system of claim 10, wherein the memory comprises further instructions executable to at least: assign values to the retrieved relevant attributes based on information known and/or inferable from (a) the associated elements in the multi-element taxonomy, (b) content of the inspected document, and/or (c) a master collection of items. 15. A data classification method, the method comprising: receiving documents comprising data entries via an input interface, at least some of the data entries having associated features represented directly in the documents; having a model registry storing at least one model stack, each model stack including at least one classification model and at least one confidence model that is separate from the at least one classification model in the respective model stack, each classification model being configured to generate proposed classifications in accordance with a multi-element taxonomy, with each element in the multi-element taxonomy denoting a respective category, each confidence model being configured to make a trust or do not trust decision relative to a proposed classification generated by the at least one classification model in the associated model stack; inspecting documents received via the input interface to identify data entries and associated features located in the inspected documents; identifying one or more model stacks from the model registry for execution on the identified data entries; executing, using processing resources including at least one processor and a memory, the at least one classification model in the identified one or more model stacks to arrive at proposed classifications for the identified data entries; executing, using the processing resources, the at least one confidence model in the identified one or more model stacks to arrive at trust or do not trust decisions for each of the arrived at proposed classifications based on a likelihood of category collision occurring when executing the at least one classification model, wherein category collision represents classification uncertainty as between two or more elements in the taxonomy for a given data entry, and wherein trust decisions indicate absences of category collisions; storing, to a data warehouse including curated and classified data elements, data entries and their associated proposed classifications, for those proposed classifications where trust decisions have been reached via execution of the at least one confidence model; and responding to queries from a computer terminal by referencing records in the data warehouse. 16. The method of claim 15, wherein each confidence model is category-specific with respect to the multi-element taxonomy. 17. The method of claim 15, wherein the model registry stores a plurality of model stacks, at least one of the model stacks including a plurality of classification models and one or more confidence models, and further comprising: implementing a form of voting to combine output from the execution of plural classification models in order to generate a single proposed classification therefrom, the form of voting being simple majority-rules voting, a form of proportional voting, or a combination thereof. 18. The method of claim 15, wherein receivable documents are heterogeneous. 19. The method of claim 15, further comprising: an attribute database configured to store attributes associated with elements in the multi-element taxonomy; wherein the memory comprises further instructions executable to at least: retrieve, for data entries having proposed classifications where trust decisions have been reached, relevant attributes from the attribute database based on granular classifications of the respective data entries to the multi-element taxonomy; and associate the retrieved relevant attributes with the respective data entries for storage to the data warehouse. 20. The method of claim 19, further comprising assigning values to the retrieved relevant attributes based on information known and/or inferable from (a) the associated elements in the multi-element taxonomy, (b) content of the inspected document, and/or (c) a master collection of items. 21. 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 documents comprising data entries via an input interface, at least some of the data entries having associated features represented directly in the documents; maintain a model registry storing at least one model stack, each model stack including at least one classification model and at least one confidence model that is separate from the at least classification model in the respective model stack, each classification model being configured to generate proposed classifications in accordance with a multi-element taxonomy, with each element in the multi-element taxonomy denoting a respective category, each confidence model being configured to make a trust or do not trust decision relative to a proposed classification generated by the at least one classification model in the associated model stack; inspect documents received via the input interface to identify data entries and associated features located in the inspected documents; identify one or more model stacks from the model registry for execution on the identified data entries; execute the at least one classification model in the identified one or more model stacks to arrive at proposed classifications for the identified data entries; execute the at least one confidence model in the identified one or more model stacks to arrive at trust or do not trust decisions for each of the arrived at proposed classifications based on a likelihood of a category collision occurring when executing the at least one classification model, wherein a given category collision represents classification uncertainty as between two or more elements in the taxonomy for a given data entry, and wherein trust decisions indicate absences of category collisions; store, to a data warehouse including curated and classified data elements, data entries and their associated proposed classifications, for those proposed classifications where trust decisions have been reached via execution of the at least one confidence model; and respond to queries from a computer terminal by referencing records in the data warehouse. 22. The non-transitory computer-readable storage medium of claim 21, wherein each confidence model is category-specific with respect to the multi-element taxonomy. 23. The non-transitory computer-readable storage medium of claim 21, wherein: the model registry stores a plurality of model stacks, at least one of the model stacks including a plurality of classification models and one or more confidence models; and a form of voting is implemented to combine output from the execution of plural classification models in order to generate a single proposed classification therefrom, the form of voting being simple majority-rules voting, a form of proportional voting, or a combination thereof. 24. The non-transitory computer-readable storage medium of claim 21, wherein receivable documents are heterogeneous. 25. The non-transitory computer-readable storage medium of claim 21, wherein: an attribute database is configured to store attributes associated with elements in the multi-element taxonomy; and further instructions are executable to at least: retrieve, for data entries having proposed classifications where trust decisions have been reached, relevant attributes from the attribute database based on granular classifications of the respective data entries to the multi-element taxonomy; associate the retrieved relevant attributes with the respective data entries for storage to the data warehouse; and assign values to the retrieved relevant attributes based on information known and/or inferable from (a) the associated elements in the multi-element taxonomy, (b) content of the inspected document, and/or (c) a master collection of items.