With the advent of the World Wide Web, and the increasing popularity of web search engines, there has been a renewed interest in information retrieval systems. In this research, we introduce a system that combines category-based and keyword-based concepts for a better information retrieval system. For improved document clustering, we proposed a document similarity measure that is based on keyword frequency in documents, but also uses an input ontology. This ontology is domain specific and includes a list of keywords organized with their degree of importance to the categories of the ontology. We evaluated the performance of this similarity measure and compared it to the standard cosine vector similarity measure. For that, we used document data with pre-determined structure as well as actual web documents. We designed a framework to generate synthetic data to model documents, and analyzed statistical attributes of documents in high dimension. For synthetic data analysis, we designed a controllable structure using various distributions of angle to specify cluster compactness and angle based inter-cluster overlap to specify cluster isolation. We address the issue of modeling text documents, and propose the use of a graph data model that is based on the concept of semantic groups. We present a mechanism by which semantic groups can be used with document processing.
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