It has become increasingly difficult to locate relevant information on the Web, even with the help of Web search engines. Two approaches to addressing the low precision and poor presentation of search results of current search tools are studied: meta-search and document categorization. Meta-search engines improve precision by selecting and integrating search results fromgeneric or domain-specific Web search engines or other resources. Document categorization promises better organization and presentation of retrieved results. This article introduces MetaSpider, a meta-search engine that has real-time indexing and categorizing functions. We report in this paper the major components of MetaSpider and discuss related technical approaches. Initial results of a user evaluation study comparing Meta- Spider, NorthernLight, and MetaCrawler in terms of clustering performance and of time and effort expended show that MetaSpider performed best in precision rate, but disclose no statistically significant differences in recall rate and time requirements. Our experimental study also reveals that MetaSpider exhibited a higher level of automation than the other two systems and facilitated efficient searching by providing the user with an organized, comprehensive view of the retrieved documents.

Information and knowledge management in a knowledge-intensive and time-critical environment presents a challenge to information technology professionals. In law enforcement, multiple data sources are used, each having different user interfaces. COPLINK Connect addresses these problems by providing one easy-to-use interface that integrates different data sources such as incident records, mug shots and gang information, and allows diverse police departments to share data easily. User evaluations of the application allowed us to study the impact of COPLINK on law-enforcement personnel as well as to identify requirements for improving the system. COPLINK Connect is currently being deployed at Tucson Police Department (TPD).

Much of the work in business and academia is performed by groups of people. While significant advancement has been achieved in enhancing individual productivity by making use of information technology, little has been done to improve group productivity. Prior research suggests that we should know more about individual differences among group members as they respond to technology if we are to develop useful systems that can support group activities. We report results of a cognitive study in which researchers were observed performing three complex information entry and indexing tasks using an Integrated Collaborative Research System. The observations have revealed a taxonomy of knowledge and cognitive processes involved in the indexing and management of information in a research collaboration environment. A detailed comparison of knowledge elements and cognitive processes exhibited by senior researchers and junior researchers has been made in this article. Based on our empirical findings, we have developed a framework to explain the information management process during research collaboration. Directions for improving design of Integrated Collaborative Research Systems are also suggested.

In this era of the Internet and the World Wide Web, the long-time topic of digital libraries has suddenly become white hot. As the Internet expands, particularly the WWW, more people are recognizing the need to search indexed collections. Digital library research projects thus have a common theme of bringing search to the Net. This is why the US government made digital libraries the flagship research effort for the National Information Infrastructure (NII), which seeks to bring the highways of knowledge to every American. As a result, the four-year, multiagency DLI was funded with roughly $1 million per year for each project (see the "Agency perspectives" sidebar). Six projects (chosen from 73 proposals) are involved in the DLI, which is sponsored by the National Science Foundation, Advanced Research Projects Agency, and the National Aeronautics and Space Administration. This issue of Computer includes project reports from these six university sites: Carnegie Mellon University, University of California at Berkeley, University of California at Santa Barbara, University of Illinois at Urbana-Champaign, University of Michigan, and Stanford University.

The problems of information overload and vocabulary differences have become more pressing with the emergence of increasingly popular Internet services. The main information retrieval mechanisms provided by the prevailing Internet WWW software are based on either keyword search (e.g., the Lycos server at CMU, the Yahoo server at Stanford) or hypertext browsing (e.g., Mosaic and Netscape). This research aims to provide an alternative concept-based categorization and search capability for WWW servers based on selected machine learning algorithms. Our proposed approach, which is grounded on automatic textual analysis of Internet documents (homepages), attempts to address the Internet search problem by first categorizing the content of Internet documents. We report results of our recent testing of a multilayered neural network clustering algorithm employing the Kohonen self-organizing feature map to categorize (classify) Internet homepages according to their content. The category hierarchies created could serve to partition the vast Internet services into subject-specific categories and databases and improve Internet keyword searching and/or browsing.

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.

An automatic indexing and concept classification approach to a multilingual (Chinese and English) bibliographic database is presented. We introduced a multi-linear termphrasing technique to extract concept descriptors (terms or keywords) from a Chinese-English bibliographic database. A concept space of related descriptors was then generated using a co-occurrence analysis technique. Like a man-made thesaurus, the system-generated concept space can be used to generate additional semantically-relevant terms for search. For concept classification and clustering, a variant of a Hopfield neural network was developed to cluster similar concept descriptors and to generate a small number of concept groups to represent (summarize) the subject matter of the database. The concept space approach to information classification and retrieval has been adopted by the aupors in other scientific databases and business applications, but multilingual information retrieval presents a unique challenge. This research reports our experiment on multilingual databases. Our system was initially developed in the MS-DOS environment, running ETEN Chinese operating system. For performance reasons, it was then tested on a UNIX-based system. Due to the unique ideographic nature of the Chinese language, a Chinese term-phrase indexing paradigm considering the ideographic characteristics of Chinese was developed as a multilingual information classification model. By applying the neural network based concept classification technique, the model presents a novel way of organizing unstructured multilingual information.

As part of the ongoing Illinois Digital Library Initiative project, this research proposes an intelligent agent approach to Web searching. In this experiment, we developed two Web personal spiders based on best first search and genetic algorithm techniques, respectively. These personal spiders can dynamically take a userâ s selected starting homepages and search for the most closely related homepages in the Web, based on the links and keyword indexing. A graphical, dynamic, Java-based interface was developed and is available for Web access. A system architecture for implementing such an agent-based spider is presented, followed by detailed discussions of benchmark testing and user evaluation results. In benchmark testing, although the genetic algorithm spider did not outperform the best first search spider, we found both results to be comparable and complementary. In user evaluation, the genetic algorithm spider obtained significantly higher recall value than that of the best first search spider. However, their precision values were not statistically different. The mutation process introduced in genetic algorithm allows users to find other potential relevant homepages that cannot be explored via a conventional local search process. In addition, we found the Java-based interface to be a necessary component for design of a truly interactive and dynamic Web agent.

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.

Our research attempted to identify the nature and causes of information management indeterminism in an online research environment and to propose solutions for alleviating this indeterminism. We conducted two empirical studies of information management activities. The first study identified the types and nature of information management indeterminism by evaluating archived texts. The second study focused on four sources of indeterminism: subject area knowledge, classification knowledge, system knowledge, and collaboration knowledge. A knowledge-based design for alleviating indeterminism, which contains a system-generated thesaurus and an inferencing engine, is also proposed in this article.