1. A computerized method for building hierarchical knowledge structures 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.
2. The method of claim 1 , wherein the calculating comprises calculating information gain for an attribute A in relation to documents S and categories C by which the documents S are grouped, and the calculating the information gain comprises handling separately a subset of the documents S, for which the attribute A is absent, to improve performance with respect to populating sub-concepts in the second ontology.