This is a preprint of an article published in the Journal of the American Society for Information Science and Technology, 53, 145-161. This study examines the problem of the judgment of information quality and cognitive authority by observing people's searching behavior in the Web. Its purpose is to understand the various factors that influence peopleâ s judgment of quality and authority in the Web, and the effects of those judgments on selection behaviors. It was found that the subjects made two distinct kinds of judgment: predictive judgment and evaluative judgment. The factors influencing each judgment of quality and authority were identified in terms of characteristics of information objects, characteristics of sources, knowledge, situation, ranking in search output, and general assumption.

The educational client interface to the ADEPT digital library is envisioned as a â learning spaceâ where digital objects are tightly integrated with instruction to improve the scientific reasoning and geographic thinking skills of undergraduates. Geography faculty, acting in the work-role of instructor, will be the primary agents utilizing this ADEPT interface. The information behavior of academics is well documented in relation to their work roles as researchers, but little has been published on their information behavior in support of instruction. We report findings from our exploratory study on the information practices of geography faculty in this context. Results suggest that the information behavior of academic geographers follows the Leckie dynamic feedback-loop model. A tentative pattern may exist in the intersection of work roles and information sources. Information seeking for instruction may be characterized as passive and formal while information seeking for research may be characterized as active and informal.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Information overload is a critical problem in World Wide Web. Category map developed based on Kohonenâ s selforganizing map (SOM) has been proven to be a promising browsing tool for the Web. The SOM algorithm automatically categorizes a large Internet information space into manageable sub-spaces. It compresses and transforms a complex information space into a two-dimensional graphical representation. Such graphical representation provides a user-friendly interface for users to explore the automatically generated mental model. However, as the amount of information increases, it is expected to increase the size of the category map accordingly in order to accommodate the important concepts in the information space. It results in increasing of visual load of the category map. Large pool of information is packed closely together on a limited size of displaying window, where local details are difficult to be clearly seen. In this paper, we propose the fisheye views and fractal views to support the visualization of category map. Fisheye views are developed based on the distortion approach while fractal views are developed based on the information reduction approach. The purpose of fisheye views are to enlarge the regions of interest and diminish the regions that are further away while maintaining the global structure. On the other hand, fractal views are an approximation mechanism to abstract complex objects and control the amount of information to be displayed. We have developed a prototype system and conducted a user evaluation to investigate the performance of fisheye views and fractal views. The results show that both fisheye views and fractal views significantly increase the effectiveness of visualizing category map. In addition, fractal views are significantly better than fisheye views but the combination of fractal views and fisheye views do not increase the performance compared to each individual technique.

The information-seeking behavior of social science faculty studying the Kurds was assessed using a questionnaire, citation analysis, and follow-up inquiry. Two specific questions were addressed: how these faculty locate relevant government information and what factors influence their seeking behavior and use of such information. Results show that besides using traditional methods for locating relevant government information, social science faculty studying the Kurds use the World Wide Web and electronic mail too for that purpose, suggesting that these faculty are aware of, and utilize, new information technology to support their research. Results also show that the information-seeking behavior of social science faculty studying the Kurds is influenced by factors similar to those influencing other social science faculty. Moreover, results also show that accessing the needed materials is a major information-seeking activity that should be added to David Ellis's behavioral model, and that faculty examined here employ a somewhat more elaborate "differentiating" information-seeking activity than the one described in the model. Some elements of interdisciplinarity of Kurdish studies as a field of research has been discovered, however, further research is required to verify that. Implications on library services and suggestions for future research are presented.

The study presents the findings of a sample survey carried out using two sub-sample populations (Rural Communities and Information providers). The main objective of the study is to explore the Community Information Needs of rural communities in Sri Lanka and their information behaviour. Fifteen categories of basic information needs of two types (â survivalâ and â strategicâ ) were identified. The nature of community information is recognized as non-bibliographic and service-oriented. The information supply position was identified as stagnated at service points, and the dynamism of the information has deteriorated within the delivery mechanisms limited to system structure. It was possible to calculate a Channel Dependency Rate (CDR) which showed that channels appropriate to provide certain categories of information were not strong and or operating as they should be. Rural citizens were also found to encounter a range of difficulties in accessing information, and it was found that these were related to geographical, structural (socio-economic and cultural) factors and personal reasons. The study proposes Community Information Centres using e-governance strategy with One Stop Shop (OSS) model, to be established at the village level using prevailing infrastructure to bridge the information gap existing in the rural areas of Sri Lanka.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This article presents a knowledge-based approach to the design of document-based retrieval systems. We conducted two empirical studies investigating the users' behavior using an online catalog. The studies revcaled a range of knowledge elements which are necessary for performing a successful search. We proposed a semantic network based representation to capture these knowledge elements. The findings we derived from our empirical studies were used to construct a knowledge-based retrieval system. We performed a laboratory experiment to calculate the search performance of our system. The experiment showed that our system out-performed a conventional retrieval system in recall and user satisfaction. The implications of our study to the design of document-based retrieval systems are also discussed in this article.

This study investigates the use of criteria to assess relevant, partially relevant and not relevant documents. Each study participant identified passages within 20 document representations that were used in making relevance judgments, judged each document representation as a whole to be relevant, partially relevant or not relevant to their information need, and explained their decisions in an interview. Analysis revealed 29 criteria, discussed positively and negatively, used by the participants when selecting passages that contributed or detracted from a document's relevance. These criteria can be grouped into 6 categories: author, abstract, content, full text, journal or publisher and personal. Results indicate that multiple criteria are used when making relevant, partially relevant and not relevant judgments. Additionally, most criteria can have both a positive or negative contribution to the relevance of a document. The criteria most frequently mentioned by study participants in this study was content, followed by criteria concerning the full text document. These findings may have implications for relevance feedback in information retrieval systems, suggesting that users give relevance feedback using multiple criteria and indicate positive and negative criteria contributions. Systems designers may want to focus on supporting content criteria followed by full text criteria as this may provide the greatest cost benefit.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic Al paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify â convergentâ concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing largescale, multiple thesauri (knowledge network).

Poster paper / The services that are provided in a library are at various levels and varieties. Library automation services such as computerized OPAC, e-mail based reference service etc., are be-ing provided. Also, Web based services like Web Opac, E-Journals, CD-ROM Collection search, Bibliographical database services such as Engineering village 2, Compendex, Chemi-cal Abstract, Web of science, are being provided.

Artificial Intelligence Lab, Department of MIS, University of Arizona / The problems of information overload and vocabulary differences have become more pressing with the emergence of the increasingly popular Internet services. The main information retrieval mechanisms provided by the prevailing Internet WWW software are based on either keyword search or hypertext browsing. Keyword search often results in low precision, poor recall, and slow response time due to the limitations of indexing and communication methods, controlled language based interfaces, and the inability of searchers themselves to articulate their needs fully. Hypertext browsing, on the other hand, allows users to explore only a very small portion of a large Internet information space. A large information space can also potentially confuse and disorient its user and it can cause the user to spend a great deal of time while learning nothing specific. This research aims to provide concept-based categorization and search capabilities for Internet WWW servers based on selected machine learning and parallel computing techniques. Our proposed approach, which is grounded on automatic textual analysis of Internet documents, attempts to address the Internet search problem by first categorizing the content of Internet documents and subsequently providing semantic search capabilities based on a concept space approach. As a first step, we propose a multi-layered neural network clustering algorithm employing the Kohonen self-organizing feature map to categorize the Internet homepages according to their content. The category hierarchies created could serve to partition the vast Internet services into subject-specific categories and databases. After individual subject categories have been created, we propose to generate domain-specific concept spaces for each subject category. The concept spaces can then be used to support concept-based information retrieval, a significant improvement over the existing keyword searching and hypertext browsing options for Internet resource discovery. As Internet information space continues to grow at the present pace, we believe this research would shed light on potentially robust and scalable solutions to the increasingly complex and urgent information access and sharing problems that are certain to emerge in the future Internet society.