• Convergence of Knowledge Management and E-Learning: the GetSmart Experience

      Marshall, Byron; Zhang, Yiwen; Chen, Hsinchun; Lally, Ann M.; Shen, Rao; Fox, Edward; Cassel, Lillian N. (ACM/IEEE, 2003)
      The National Science Digital Library (NSDL), launched in December 2002, is emerging as a center of innovation in digital libraries as applied to education. As a part of this extensive project, the GetSmart system was created to apply knowledge management techniques in a learning environment. The design of the system is based on an analysis of learning theory and the information search process. Its key notion is the integration of search tools and curriculum support with concept mapping. More than 100 students at the University of Arizona and Virginia Tech used the system in the fall of 2002. A database of more than one thousand student-prepared concept maps has been collected with more than forty thousand relationships expressed in semantic, graphical, node-link representations. Preliminary analysis of the collected data is revealing interesting knowledge representation patterns.
    • HelpfulMed: Intelligent Searching for Medical Information over the Internet

      Chen, Hsinchun; Lally, Ann M.; Zhu, Bin; Chau, Michael (Wiley Periodicals, Inc, 2003-05)
      Medical professionals and researchers need information from reputable sources to accomplish their work. Unfortunately, the Web has a large number of documents that are irrelevant to their work, even those documents that purport to be â medically-related.â This paper describes an architecture designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, or â concept space,â and Kohonen-based Self-Organizing Map (SOM) technologies to provide searchers with finegrained results. Initial results indicate that these systems provide complementary retrieval functionalities. HelpfulMed not only allows users to search Web pages and other online databases, but also allows them to build searches through the use of an automatic thesaurus and browse a graphical display of medical-related topics. Evaluation results for each of the different components are included. Our spidering algorithm outperformed both breadth-first search and PageRank spiders on a test collection of 100,000 Web pages. The automatically generated thesaurus performed as well as both MeSH and UMLSâ systems which require human mediation for currency. Lastly, a variant of the Kohonen SOM was comparable to MeSH terms in perceived cluster precision and significantly better at perceived cluster recall.
    • NanoPort: A Web Portal for Nanoscale Science and Technology

      Chau, Michael; Chen, Hsinchun; Qin, Jailun; Zhou, Yilu; Sung, Wai-Ki; Chen, Mark; Qin, Yi; McDonald, Daniel M.; Lally, Ann M. (ACM/IEEE-CS, 2002)
      Areas related to nanotechnology, or nanoscale science and engineering (NSSE), have experienced tremendous growth over the past few years. While there are a large variety of useful resources available on the Web, such information are usually distributed and difficult to locate, resulting in the problem of information overload. To address the problem, we developed the NanoPort system, an integrated Web portal aiming to provide a one-stop shopping service to satisfy the information needs of researchers and practitioners in the field of NSSE [1]. We believe that the approaches taken also can be applied to other domains.
    • The Use of Dynamic Contexts to Improve Casual Internet Searching

      Leroy, Gondy; Lally, Ann M.; Chen, Hsinchun (ACM, 2003-07)
      Research has shown that most usersâ online information searches are suboptimal. Query optimization based on a relevance feedback or genetic algorithm using dynamic query contexts can help casual users search the Internet. These algorithms can draw on implicit user feedback based on the surrounding links and text in a search engine result set to expand user queries with a variable number of keywords in two manners. Positive expansion adds terms to a userâ s keywords with a Boolean â and,â negative expansion adds terms to the userâ s keywords with a Boolean â not.â Each algorithm was examined for three user groups, high, middle, and low achievers, who were classified according to their overall performance. The interactions of users with different levels of expertise with different expansion types or algorithms were evaluated. The genetic algorithm with negative expansion tripled recall and doubled precision for low achievers, but high achievers displayed an opposed trend and seemed to be hindered in this condition. The effect of other conditions was less substantial.