1. Introduction Information Management systems are designed to retrieve information efficiently. Such systems typically provide an interface in which users formulate a search query to get a list of possible results. If users do not find what they are looking for, they must carefully refine and resubmit their query, and review the results again. Human memory tends to retrieve information in an associative way [1]. Our hypothesis is that storing an information set in a similar way— by using an associative semantic network to represent data as nodes and the relations between them as links [2]— will improve the effectiveness of information browsing behaviours through associative retrieval. Direct search is fast when appropriate keywords are used, but it can fail when users do not have a clear idea of their target. With associative networks, users begin as before with a direct search, but can then pick one of the result nodes and browse from there to other related nodes. By following links in the “semantic direction” of the target, users can “home in” on what they seek. Browsing can also be useful when searching for topics only vaguely defined in the user’s mind, or for getting a general idea of the thematic content of an information space. Method We examined whether similarity networks can aid retrieval tasks in large document corpora. We built an associative similarity network from two corpora of 2000 documents each, based on document keywords extracted with the tfidf algorithm. Each network node represents a single document, and is connected to other similar nodes with links weighted to reflect the degree of similarity. A common set of browsing and searching tasks were presented to 24 subjects, requesting them to retrieve documents which fit certain requirements. The interface used for the experiment is presented in Figure 1. For each task, half of the subjects used a standard search engine only, while the other half used the search engine enhanced by a browsing option that showed them the nearest neighbours in the network ranked by similarity. Quantitative and qualitative performance measures were taken to compare the two conditions. Analysis Performance in the tasks was evaluated using two metrics: 1.Correct answers to tasks were determined by two referees who reviewed user selections to judge whether each selection answered the task correctly. 2.Each selection was given an automatic score based on the frequency with which it was selected as an answer. There were 3 types of task: 1. Direct search tasks in which the target is well defined, so that it is easy to find using a conventional search engine. 2. Browsing tasks in which the target is not well defined, so that it is difficult find using a conventional search engine. 3. Topic tasks which ask the user to find all topics related to a particular theme discussed in the document corpus. Conclusions The results indicate that user performance improved when they were offered associative browsing in addition to conventional search. Improved performance was indicated by both performance measures, with significantly better results for browsing-type tasks. Contrary to our initial expectations, users did not tend to use the browse button to “home in” on targets. Instead, users used the browse button to find similar documents to one already found. Users typically found the first answer using a conventional search, and then used the browsing option to look for similar results. We conclude that semantic similarity networks can be used to improve user browsing behaviour, particularly in tasks for which the target is not well defined. More specifically, they were used to search exhaustively for related information given a known target. Improved Browsing with Semantic Networks Joel Lanir, Mike Huggett, Holger Hoos, Ronald Rensink Department of Computer Science, University of British Columbia Fig. 1. Experimental interface. Users were presented with a task (at the bottom of the screen) and were required to find the articles best suited to the task. References [1] Anderson, J. R. (1983) A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior 22(3):261–295. [2] Crestani, F. (1997) Applications of spreading activation techniques in Information Retrieval, Artificial Intelligence Review 11(6):453-582. calculated score results of search and browse interfaces example: Find articles that discuss reporters (e.g. Judith Miller) going to jail for not revealing their source. Subjects used the browse interface button in 85.8% of assigned browsing tasks. 80.0% of browse-button usage involved documents already selected as answers for a task. example: Find articles criticizing U.S. policies in the Iraqi war. example: Determine the most common topics related to technology. Results Acknowledgements We would like to thank Precarn for the financial support.