Robin R. Sewell

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

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 subcategory of Yahool) 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 homepages 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.

Medical Data Mining on the Internet: Research on a Cancer Information System

This paper discusses several data mining algorithms and techniques thatwe have developed at the University of Arizona Artificial Intelligence Lab.We have implemented these algorithms and techniques into severalprototypes, one of which focuses on medical information developed incooperation with the National Cancer Institute (NCI) and the University ofIllinois at Urbana-Champaign. We propose an architecture for medicalknowledge information systems that will permit data mining across severalmedical information sources and discuss a suite of data mining tools that weare developing to assist NCI in improving public access to and use of theirexisting vast cancer information collections.

Exploring the use of concept spaces to improve medical information retrieval

This research investigated the application of techniques successfully used in previous information retrieval research, to the more challenging area of medical informatics. It was performed on a biomedical document collection testbed, . CANCERLIT, provided by the National Cancer Institute NCI , which contains information on all types of cancer therapy. The quality or usefulness of terms suggested by three different thesauri, one based on MeSH terms, one based solely on . terms from the document collection, and one based on the Unified Medical Language System UMLS Metathesaurus, was explored with the ultimate goal of improving CANCERLIT information search and retrieval. Researchers affiliated with the University of Arizona Cancer Center evaluated lists of related terms suggested by different thesauri for 12 different directed searches in the CANCERLIT testbed. The preliminary results indicated that among the thesauri, there were no statistically significant differences in either term recall or precision. Surprisingly, there was almost no overlap of relevant terms suggested by the different thesauri for a given search. This suggests that recall could be significantly improved by using a combined thesaurus approach. q 2000 Elsevier Science B.V. All rights reserved.

Concept-based searching and browsing: a geoscience experiment

In the recent literature, we have seen the expansion of information retrieval techniques to include a variety of different collections of information. Collections can have certain characteristics that can lead to different results for the various classification techniques. In addition, the ways and reasons that users explore each collection can affect the success of the information retrieval technique. The focus of this research was to extend the application of our statistical and neural network techniques to the domain of geological science information retrieval. For this study, a test bed of 22,636 geoscience abstracts was obtained through the NSF/DARPA/NASA funded Alexandria Digital Library Initiative project at the University of California at Santa Barbara. This collection was analyzed using algorithms previously developed by our research group: concept space algorithm for searching and a Kohonen self-organizing map (SOM) algorithm for browsing. Included in this paper are discussions of our techniques, user evaluations and lessons learned.

Internet browsing and searching (poster): user evaluations of category map and concept space techniques

pages indicated that there was limited overlap between The Internet provides an exceptional testbed for develthe homepages retrieved by the subject-suggested and oping algorithms that can improve browsing and searchthesaurus-suggested terms. Since the retrieved homeing large information spaces. Browsing and searching pages for the most part were different, this suggests that tasks are susceptible to problems of information overa user can enhance a keyword-based search by using load and vocabulary differences. Much of the current an automatically generated concept space. Subjects esresearch is aimed at the development and refinement of pecially liked the level of control that they could exert algorithms to improve browsing and searching by adover the search, and the fact that the terms suggested dressing these problems. Our research was focused on by the thesaurus were ‘‘real’’ ( i.e., originating in the discovering whether two of the algorithms our research homepages) and therefore guaranteed to have retrieval group has developed, a Kohonen algorithm category success. 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

Managing Consortium Resource Access Using Athens Authentication

ABSTRACT The Arizona Health Information Network (AZHIN) needed a single username and password system to provide access to its resources. The existing vendor-based password system was cumbersome for users, administratively difficult to manage, and provided less than adequate resource security. AZHIN elected to use the Athens authentication system. This article describes the features of Athens and how AZHIN used it to implement a single sign-on system.

Medical information access for the new millennium: UMLS-enhanced semantic parsing and personalized medical agent

1 Management Information Systems Department, University of Arizona, Tucson, AZ 85721, 520-621-3927, ktolle@bpa.arizona.edu 2 Management Information Systems Department, University of Arizona, Tucson, AZ 85721, 520-621-3927, gleroy@bpa.arizona.edu 3 Management Information Systems Department, University of Arizona, Tucson, AZ 85721, 520-621-6219, rrs@ai.bpa.arizona.edu 4 Management Information Systems Department, University of Arizona, Tucson, AZ 85721, 520-626-9239, yfang@bpa.arizona.edu 5 Management Information Systems Department, University of Arizona, Tucson, AZ 85721, 520-621-3927, dmitri@bpa.arizona.edu 6 Management Information Systems Department, University of Arizona, Tucson, AZ 85721, 520-626-9239, zstavri@irls.arizona.edu 7 Management Information Systems Department, University of Arizona, Tucson, AZ 85721, 520-621-4153, hchen@bpa.arizona.edu