Capturing the meaning of internet search queries can significantly improve the effectiveness of search retrieval. Users often face with the problem of finding the relevant answer on the result pages for their internet search, particularly, when the posted query is ambiguous. The orientation of the user can be greatly facilitated, if answers are grouped into topics of a fixed subject taxonomy. In this manner, the original problem can be transformed to the labelling of queries-and consequently, the answers - with the topic names. This is clearly a categorization problem, i.e. it requires supervised machine learning. This paper introduces our approach, called Ferrety algorithm that performs topic assignment, which also works when there is no directly available training data that describes the semantics of the subject taxonomy. It is presented via the example of ACM KDD Cup 2005 problem, where Ferrety was awarded for precision and creativity.