The article has been reviewed and accepted for publication in Knowledge Organization 33 (2006) / This is a preprint of a paper to be published in Knowledge Organization. The paper gives a short overview of the history of use of UDC in Internet subject gateways (SGs) with an English interface, from 1993 to 2006. There were in total, nine quality controlled SGs that were functional for shorter or longer periods of time. Their typology and functionality is described. Quality SGs have evolved and the role of classification has changed accordingly from supporting subject organization on the interface and automatic categorization of resources, towards supporting a semantic linking, control and vocabulary mapping between different indexing systems in subject hubs and federated SGs. In this period, many SGs ceased to exist and little information remains available regarding their status. SGs currently using UDC, for some part of their resource organization, do not use a UDC subject hierarchy at the interface and its role in resource indexing has become more difficult to observe. Since 2000, UDC has become more prevalent in East European SGs, portals and hubs, which are outside the scope of this research. This paper is an attempt to provide a record on this particular application of UDC and to offer some consideration of the changes in requirements when it comes to the use of library classification in resource discovery.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information on the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify similarities in consumer interests. In this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score.We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may “dilute” the data used to infer user preferences and lead to degradation in recommendation performance.

Artificial Intelligence Lab, Department of MIS, University of Arizona / 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.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Search engines and data mining are two research areas that have experienced significant progress over the past few years. Overwhelming acceptance of the Internet as a primary medium for content delivery and business transactions has created unique opportunities and challenges for researchers. The richness of the webâ s multimedia content, the reach and timeliness of web-based publication, the proliferation of e-commerce activities and the potential for wireless web delivery have generated many interesting research problems. Technical, system, organizational and social research approaches are all needed to address these research problems. Many interesting webretrieval and mining research topics have emerged recently. These include, but are not limited to, the following: text and data mining on the web, web visualization, web intelligence and agents, web-based decision support and knowledge management, wireless web retrieval and visualization, web-based usability methodology, web-based analysis for eCommerce applications. This special issue consists of nine papers that report research in web retrieval and mining.

The authors use an exploratory project involving Web resources related to Alzheimer’s Disease to explore ways in RDF metadata can more effectively translate the virtues of the traditional vertical file to a Web environment form using Semantic Web descriptive standards. In so doing, they argue against the separation of “bibliographic control” from the socially-embedded institutional practices of reference work, collection development, and the management of information ephemera. Libraries of the future will use specific Web technologies that lend themselves to sophisticated and rigorous knowledge structures, and link them with librarians’ skills in information harvesting and evaluation.

This paper describes an exploratory, multisite case study of the production of textual content for state agency Web sites. The qualitative field study explored internal agency Web staff characterizations of textual Web content and staff perceptions of factors affecting the production of content. Study results suggest that staff characterize content in terms of its format, its style age, its rate of change, its degree of change, its owner, and the degree to which it is sensitive. Staff described nine factors affecting content production including information intensity, public education mission, public inquiry burden, top-down directives, existing maintenance burden, review and approval process, resources, management interest and goals, and support from other program staff. A better understanding of how internal agency staff perceive and treat content is important because staff play a large role in determining what content is produced and what characteristics the content contains. The inclusion or exclusion of certain characteristics in content has important implications for information usability, costs, citizen participation in agency policymaking, government transparency, and public trust in government.

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