Artificial Intelligence Lab, Department of MIS, University of Arizona / This article presents research on the design of knowledge-based document retrieval systems. We adopted a semantic network structure to represent subject knowledge and classification scheme knowledge and modeled experts' search strategies and user modeling capability as procedural knowledge. These functionalities were incorporated into a prototype knowledge-based retrieval system, Metacat. Our system, the design of which was based on the blackboard architecture, was able to create a user profile, identify task requirements, suggest heuristics-based search strategies, perform semantic-based search assistance, and assist online query refinement.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Law enforcement agencies across the United States have begun to focus on innovative knowledge management technologies to aid in the analysis of criminal information. The use of such technologies can serve as intelligence tools to combat criminal activity by aiding in case investigation or even by predicting criminal activity. Funded by the National Institute of Justice, the University of Arizonaâ s Artificial Intelligence Lab has teamed with the Tucson Police Department (TPD) to develop the Coplink Concept Space application, which serves to uncover relationships between different types of information currently existing in TPDâ s records management system. A small-scale field study involving real law enforcement personnel indicates that the use of Coplink Concept Space can reduce the time spent on the investigative task of linking criminal information as well as provide strong arguments for expanded development of similar knowledge management systems in support of law enforcement.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Nanoscale science and engineering (NSE) and related areas have seen rapid growth in recent years. The speed and scope of development in the field have made it essential for researchers to be informed on the progress across different laboratories, companies, industries and countries. In this project, we experimented with several analysis and visualization techniques on NSE-related United States patent documents to support various knowledge tasks. This paper presents results on the basic analysis of nanotechnology patents between 1976 and 2002, content map analysis and citation network analysis. The data have been obtained on individual countries, institutions and technology fields. The top 10 countries with the largest number of nanotechnology patents are the United States, Japan, France, the United Kingdom, Taiwan, Korea, the Netherlands, Switzerland, Italy and Australia. The fastest growth in the last 5 years has been in chemical and pharmaceutical fields, followed by semiconductor devices. The results demonstrate potential of information-based discovery and visualization technologies to capture knowledge regarding nanotechnology performance, transfer of knowledge and trends of development through analyzing the patent documents.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Presentation given by Hsinchun Chen at the NASA Meeting during PMSEP3 on the future of knowledge management. The presentation describes research performed by the Artificial Intelligence Lab at the University of Arizona to create a Perpetual Self-Organizing (PSO) approach to knowledge management funded by NSF, DARPA, NASA, NIJ, and NIH.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Two studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of five computational models of online document retrieval. These models were then incorporated into the design of an "intelligent" document-based retrieval system. Following a discussion of this system, we discuss the broader implications of our research for the design of information retrieval systems.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. By using an image as its interface, the prototype system addresses a troublesome aspect of traditional retrieval models, which require users to have complete knowledge of the low-level features of an image. In addition we describe an experiment to validate the performance of this image retrieval system against that of human subjects in an effort to address the scarcity of research evaluating performance of an algorithm against that of human beings. The results of the experiment indicate that the system could do as well as human subjects in accomplishing the tasks of similarity analysis and image categorization. We also found that under some circumstances texture features of an image are insufficient to represent a geographic image. We believe, however, that our image retrieval system provides a promising approach to integrating image processing techniques and information retrieval algorithms.

Artificial Intelligence Lab, Department of MIS, Univeristy of Arizona / Fear about identity verification reached new heights since the terrorist attacks on Sept. 11, 2001, with national security issues related to detecting identity deception attracting more interest than ever before. Identity deception is an intentional falsification of identity in order to deter investigations. Conventional investigation methods run into difficulty when dealing with criminals who use deceptive or fraudulent identities, as the FBI discovered when trying to determine the true identities of 19 hijackers involved in the attacks. Besides its use in post-event investigation, the ability to validate identity can also be used as a tool to prevent future tragedies. Here, we focus on uncovering patterns of criminal identity deception based on actual criminal records and suggest an algorithmic approach to revealing deceptive identities.

Artificial Intelligence Lab, Department of MIS, University of Arizona / An experiment with an AI-based software agent shows that it can help users organize and consolidate ideas from electronic brainstorming. The agent recalled concepts as effectively as experienced human meeting facilitators and in a fifth of the time.

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

Artificial Intelligence Lab, Department of MIS, University of Arizona / Introduction to Special Issue of JASIS on AI Techniques for Emerging Information Systems Applications in which five articles report research in adopting artificial intelligence techniques for emerging information systems applications.