  A major computational burden, while performing document clustering, is the
calculation of similarity measure between a pair of documents. Similarity
measure is a function that assign a real number between 0 and 1 to a pair of
documents, depending upon the degree of similarity between them. A value of
zero means that the documents are completely dissimilar whereas a value of one
indicates that the documents are practically identical. Traditionally,
vector-based models have been used for computing the document similarity. The
vector-based models represent several features present in documents. These
approaches to similarity measures, in general, cannot account for the semantics
of the document. Documents written in human languages contain contexts and the
words used to describe these contexts are generally semantically related.
Motivated by this fact, many researchers have proposed semantic-based
similarity measures by utilizing text annotation through external thesauruses
like WordNet (a lexical database). In this paper, we define a semantic
similarity measure based on documents represented in topic maps. Topic maps are
rapidly becoming an industrial standard for knowledge representation with a
focus for later search and extraction. The documents are transformed into a
topic map based coded knowledge and the similarity between a pair of documents
is represented as a correlation between the common patterns. The experimental
studies on the text mining datasets reveal that this new similarity measure is
more effective as compared to commonly used similarity measures in text
clustering.
