Abstract

The recent explosion of on-line information in Digital Libraries and on the World Wide Web has given rise to a number of query-based search engines and manually constructed topical hierarchies. However, these tools are quickly becoming inadequate as query results grow incomprehensibly large and manual classification in topic hierarchies creates an immense bottleneck. We address these problems with a system for topical information space navigation that combines the query-based and taxonomic systems. We employ machine learning techniques to create dynamic document categorizations based on the full-text of articles that are retrieved in response to users' queries. Our system, named SONIA (Service for Organizing Networked Information Autonomously), has been implemented as part of the Stanford Digital Libraries Testbed. It employs a combination of technologies that takes the results of queries to networked information sources and, in real-time, automatically retrieve, parse and organize the...

Citations