Search (4 results, page 1 of 1)

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Abstract

Libraries, private and public, offer valuable resources to library patrons. As of today, the only way to locate information archived exclusively in libraries is through their catalogs. Library patrons, however, often find it difficult to formulate a proper query, which requires using specific keywords assigned to different fields of desired library catalog records, 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 turns library patrons away from directly querying library catalogs; instead, they rely on Web search engines to perform their searches first, and upon obtaining the initial information (e.g., titles, subject headings, or authors) on the desired library materials, they query library catalogs. This searching strategy is an evidence of failure of today's library systems. In solving this problem, we propose an enhanced library system, which allows partial, similarity matching of (a) tags defined by ordinary users at a folksonomy site that describe the content of books and (b) unrestricted keywords specified by an ordinary library patron in a query to search for relevant library catalog records. The proposed library system allows patrons posting 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 that achieved by current library search engines.

Language

e

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Abstract

E-mail messages are unquestionably one of the most popular communication media these days. Not only are they fast and reliable but also free in general. Unfortunately, a significant number of e-mail messages received by e-mail users on a daily basis are spam. This fact is annoying since spam messages translate into a waste of the user's time in reviewing and deleting them. In addition, spam messages consume resources such as storage, bandwidth, and computer-processing time. Many attempts have been made in the past to eradicate spam; however, none has proven highly effective. In this article, we propose a spam e-mail detection approach, called SpamED, which uses the similarity of phrases in messages to detect spam. Conducted experiments not only verify that SpamED using trigrams in e-mail messages is capable of minimizing false positives and false negatives in spam detection but it also outperforms a number of existing e-mail filtering approaches with a 96% accuracy rate.

Language

e

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Language

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Language

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