16. A computer program product, encoded on a computer-readable medium, operable to cause data processing apparatus to perform operations comprising: receiving a first ontology including initial categories, an indication of sample data for a given category of the initial categories, and an indication of symbolic knowledge for the given category; and populating the first ontology with new features to form a second ontology for use in document categorization, the populating comprising determining the new features from the sample data using a statistical machine learning process and retaining the new features and the symbolic knowledge within the second ontology in association with the given category, wherein the sample data comprises sample documents, and the determining comprises: extracting attributes from the sample documents; calculating a statistical concept-distance metric for the attributes; selecting a first subset of the attributes that are more distinguishing with respect to the sample documents based on the calculated statistical concept-distance metric for the attributes and a first user-controllable input; and selecting a second subset of the first attribute subset based on the given category and a relevance measure for attributes in the first attribute subset with respect to the given category, the relevance measure being affected by a second user-controllable input; wherein the new features comprise attributes in the second attribute subset.
16. A computer program product, encoded on a computer-readable medium, operable to cause data processing apparatus to perform operations comprising: receiving a first ontology including initial categories, an indication of sample data for a given category of the initial categories, and an indication of symbolic knowledge for the given category; and populating the first ontology with new features to form a second ontology for use in document categorization, the populating comprising determining the new features from the sample data using a statistical machine learning process and retaining the new features and the symbolic knowledge within the second ontology in association with the given category, wherein the sample data comprises sample documents, and the determining comprises: extracting attributes from the sample documents; calculating a statistical concept-distance metric for the attributes; selecting a first subset of the attributes that are more distinguishing with respect to the sample documents based on the calculated statistical concept-distance metric for the attributes and a first user-controllable input; and selecting a second subset of the first attribute subset based on the given category and a relevance measure for attributes in the first attribute subset with respect to the given category, the relevance measure being affected by a second user-controllable input; wherein the new features comprise attributes in the second attribute subset. 29. The computer program product of claim 16 , wherein the determining comprises determining a variance in the calculated statistical concept-distance metric for the attributes, and wherein the selecting the first subset comprises selecting the first subset based on the first user-controllable input combined with the determined variance in the calculated statistical concept-distance metric for the attributes.