11. The method of claim 7 , including: classifying the set of documents into classes independent of the second partition; generating a contingency table for comparing the categories of the different second taxonomy with the classes of the set of documents; and identifying a set of mutually different recent categories using the contingency table.
7. A method executed by a computer processor, comprising: extracting a set of documents related to a specified topic from a data warehouse; automatically generating a first taxonomy through a feature space derived from the set of documents, wherein the feature space includes at least one of unstructured data; structured data, and annotations derived from text of the set of documents, and the first taxonomy provides a first partition of the set of documents according to the first taxonomy; using domain-specific knowledge to re-partition the set of documents to provide a second partition of the set of documents; using a first taxonomy to categorize the set of documents into a first set of categories; creating a second set of categories of the set of documents, wherein the second set of categories are independent of the second partition based on at least one of unstructured data, structured data, and annotations derived from text in the set of documents; constructing a contingency table having the first set of categories along a first axis and the second set of categories along a second axis, wherein the contingency table includes cells having respective actual values and for which respective expected values are computed, and includes a cell having trending information; displaying the first set of categories along a first axis and the second set of categories along a second axis on a display device; comparing the expected value of a cell against the actual value of a cell to identify a category of interest; computing a degree of significance for the actual value of the cell; identifying a relationship between at least two different categories using the contingency table; using the contingency table and trending information to identify a recent category with respect to some pre-determined date; creating a second taxonomy different from and independent of the first taxonomy for the set of documents according to the second partition so that the different second taxonomy incorporates the domain-specific knowledge; comparing each of a plurality of categories in the first partition of the set of documents with each of a plurality of categories in the second partition of the set of documents; and combining the first taxonomy with the second taxonomy by merging classes in the first taxonomy with classes in the second taxonomy.