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The rapid growth of on-line information on the Internet and other platforms presents a great chal-lenge to information retrieval systems. However, improvements in information retrieval technol-ogy have not kept pace with the growth in available information. State-of-the-art information retrieval systems rely exclusively on literal matching of keywords in queries and documents. In order to shoulder a greater load in searching for information and to improve the effectiveness of searches, information retrieval systems need the ability to automatically match user queries and documents without relying on literal equivalence. This dissertation presents a framework for generalization through the incorporation of semantic knowledge into information retrieval systems. The semantic knowledge is extracted from an online Webster's dictionary and a thesaurus named WordNet. The use of semantic knowledge is guided by constructing structured representations of the queries and the text, which identify the roles being played by the salient words. Generalization is controlled by the use of application-dependent matching rules. The dissertation describes the architecture, implementation and evalua