<blockquote>
  <p>How and where do you get the categories - pre-defined labels?</p>
</blockquote>

<p>There are many benchmark text datasets with taxonomy and ontology information. <a href="http://en.wikipedia.org/wiki/WordNet" rel="nofollow">Wordnet</a> is one such a popular benchmark dataset used in text analysis research. <a href="http://acl.ldc.upenn.edu/P/P94/P94-1019.pdf" rel="nofollow">This</a> is the first paper that focused on using taxonomy to arrive at a semantic similarity for text analysis on Wordnet. . <a href="http://arxiv.org/pdf/1105.5444.pdf?origin=publication_detail" rel="nofollow">This</a> is a more recent good paper dealing with similar objective.</p>

<blockquote>
  <p>Is it possible to plug-in an ontology, taxonomy for that and go as granular as needed?</p>
</blockquote>

<p>Yes. There is a research subfield that deals with arriving at a semantic similarity based on taxonomy and ontology that exist among the concepts (in this case, concepts in text documents). This <a href="http://dspace.library.drexel.edu/bitstream/1860/2754/1/2006175421.pdf" rel="nofollow">paper</a> provides an overview and comparative study of techniques that bring in ontology and taxonomy into measuring similarities among documents. //go as granular as needed// - Yes, you can do so, by arriving at a new similarity measure that controls the granularity. Many research work pertain to this. This <a href="http://www.researchgate.net/publication/224041454_Ontology-based_semantic_similarity_A_new_feature-based_approach/file/e0b495149d58680c80.pdf" rel="nofollow">paper</a> is a recent example.</p>

<blockquote>
  <p>Do we use n-grams in this case for approximate matching?</p>
</blockquote>

<p>Yes possible, but the aforementioned papers use less granular approaches that model concepts from documents. Most of them use tf-idf and not n-grams of terms.</p>
Document multi-label classification - where do you get the labels? Ontology?
<p>I am familiar with data mining techniques but not so much with text mining or Web mining.</p>

<p>Here is a simple task: classify articles into a set of categories.
Let us assume, I extracted text content of the article and processed it.</p>

<p>How and where do you get the categories - pre-defined labels? Is it possible to plug-in an ontology, taxonomy for that and go as granular as needed? Classification task will be a multi-label classification.</p>

<p>Do we use n-grams in this case for approximate matching?</p>

<p>Currently I have themes and named entities extracted from the text. 
Can I use Vowpal Wabbit for that?</p>