A perfect intelligence would not confine itself to one order of thought,
but would simultaneously regard a group of objects as
classified in all the ways of which they are capable.

Stanley Jevons


Automatic text categorization is the task of assigning any of a set of predefined categories to a document. The prevailing approach is that of supervised machine learning, defined as assigning category labels to new documents based on the likelihood suggested by a training set of labeled documents. This approach consists in the application of statistical methods such as Support Vector Machines, k-Nearest Neighbor classifiers, Neural Networks, Linear Least-Squares Fit mappings and Naive Bayes classifiers.
Our approach is to use Common Sense Computing to make the categorization more accurate and reliable. A major challenge that needs to be overcome for enabling auto-categorization is that the currently employed blending process requires a manual estimation of weighting factors. So, in effect, for each set of documents or each new ontology the present system needs new calibration. As a commercial product the envisaged system needs to self-configure and be available as a service whose design and content can dynamically adapt to the end-user.