Libraries, private or public, offer valuable resources to library patrons. As of today the only way to locate information archived exclusively in libraries is by searching their catalogs. Library patrons, however, often find it difficult to formulate a proper query, which requires using rigid subject terms chosen by the Library of Congress, to obtain relevant results. These improperly formulated queries often yield irrelevant results or no results at all. This negative experience in dealing with existing library systems turn library patrons away from library catalogs; instead, they rely on Web search engines to perform their searches first and upon obtaining the initial information (such as book/manuscript titles, subject areas, authors, etc.) on the desired library materials, they consult the library catalog. This searching phenomenon is an evidence of failure of today’s library systems. To solve this problem, we propose an enhanced library system, which relies on the Fuzzy Set Information Retrieval model which allows partial, similarity matching of (i) descriptive tags that describe the content of books recommended by ordinary users at a folksonomy site and (ii) keywords in a user’s query to improve the searches performed on existing library catalogs. The proposed library system allows patrons to post a query Q using commonly-used words and ranks the retrieved results according to their degrees of resemblance with Q while maintaining the query processing time comparable with existing Web search engines.