Scalable Collection Summarization and Selection

摘要

Information retrieval over the Internet increasingly requires the filtering of thousands of information sources. As the number and variety of sources increases, new ways of automatically summarizing, discovering, and selecting sources relevant to a user's query are needed. Pharos is a highly scalable distributed architecture for locating heterogeneous information sources. Its design is hierarchical, thus allowing it to scale well as the number of information sources increases. We demonstrate the feasibility of the Pharos architecture using 2500 Usenet newsgroups as separate collections. Each news-group is summarized via automated Library of Congress classification. We show that using Pharos as an intermediate retrieval mechanism provides acceptable accuracy of source selection compared to selecting sources using complete classification information, while maintaining good scalability. This implies that hierarchical distributed metadata and automated classification are potentially useful paradigms to address scalability problems in large-scale distributed information retrieval applications.