The Semantic Web would enable new ways of doing business on the<br>Web 
      that require development of advanced business document<br>integration 
      technologies performing intelligent document<br>transformation. The 
      documents use different vocabularies that<br>consist of large 
      hierarchies of terms. Accordingly, vocabulary<br>mapping and 
      transformation becomes an important task in the whole<br>business 
      document transformation process. It includes several<br>subtasks: map 
      discovery, map representation, and map execution<br>that must be 
      seamlessly integrated into the document integration<br>process. In this 
      paper we discuss the process of discovering the<br>maps between two 
      vocabularies assuming availability of two sets of<br>documents, each 
      using one of the vocabularies. We take the<br>vocabularies of product 
      classification codes as a playground and<br>propose a reusable map 
      discovery technique based on Bayesian text<br>classification approach. 
      We show how the discovered maps can be<br>integrated into the document 
      transformation process.