Internet browsing and searching: User evaluations of category map and concept space techniques

Abstract

The Internet provides an exceptional testbed for developing algorithms that can improve browsing and searching large information spaces. Browsing and searching tasks are susceptible to problems of information overload and vocabulary differences. Much of the current research is aimed at the development and refinement of algorithms to improve browsing and searching by addressing these problems. Our research was focused on discovering whether two of the algorithms our research group has developed, a Kohonen algorithm category map for browsing, and an automatically generated concept space algorithm for searching, can help improve browsing and/or searching the Internet. Our results indicate that a Kohonen self‐organizing map (SOM)‐based algorithm can successfully categorize a large and eclectic Internet information space (the Entertainment sub‐category of Yahoo!) into manageable sub‐spaces that users can successfully navigate to locate a homepage of interest to them. The SOM algorithm worked best with browsing tasks that were very broad, and in which subjects skipped around between categories. Subjects especially liked the visual and graphical aspects of the map. Subjects who tried to do a directed search, and those that wanted to use the more familiar mental models (alphabetic or hierarchical organization) for browsing, found that the map did not work well. The results from the concept space experiment were especially encouraging. There were no significant differences among the precision measures for the set of documents identified by subject‐suggested terms, thesaurus‐suggested terms, and the combination of subject‐ and thesaurus‐suggested terms. The recall measures indicated that the combination of subject‐ and thesaurus‐suggested terms exhibited significantly better recall than subject‐suggested terms alone. Furthermore, analysis of the home pages indicated that there was limited overlap between the homepages retrieved by the subject‐suggested and thesaurus‐suggested terms. Since the retrieved homepages for the most part were different, this suggests that a user can enhance a keyword‐based search by using an automatically generated concept space. Subjects especially liked the level of control that they could exert over the search, and the fact that the terms suggested by the thesaurus were “real” (i.e., originating in the homepages) and therefore guaranteed to have retrieval success. © 1998 John Wiley & Sons, Inc.

Using Category Information for Relationship Exploration in Textual Data

“…Visualization is used on large textual data sets primarily to show overviews or patterns in the data (Wise et al, 1995;Chen et al, 1998;Eick, 1994). However, with the exception of showing similarities, there has not been much work on revealing relationships between categories of textual data.…”

Visualizing criminal relationships: comparison of a hyperbolic tree and a hierarchical list

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HelpfulMed: Intelligent searching for medical information over the internet

“…The use of automatically generated thesauri has helped alleviate the problem of synonymy by adding more associative phrases to keyword indexes (Chen et al, 1998;Houston et al, 2000). However, a major problem resulting from this approach concerns the introduction of noise into the indexing process since some of the phrases may have meanings that are different from those intended.…”

“…arizona.edu/helpfulmed). In developing this system we combined existing AI Lab techniques for information retrieval using spider technology, noun phrase indexing (Tolle & Chen, 2000), automatic thesaurus generation (Concept Space) (Houston et al, 2000), data visualization (Chen et al, 1998), and a meta search tool designed to search the Invisible Web of databases. These techniques have been significantly modified and enhanced for medical information retrieval.…”