Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper describes the development and testing of the Medical Concept Mapper, a tool designed to facilitate access to online medical information sources by providing users with appropriate medical search terms for their personal queries. Our system is valuable for patients whose knowledge of medical vocabularies is inadequate to find the desired information, and for medical experts who search for information outside their field of expertise. The Medical Concept Mapper maps synonyms and semantically related concepts to a user's query. The system is unique because it integrates our natural language processing tool, i.e., the Arizona (AZ) Noun Phraser, with human-created ontologies, the Unified Medical Language System (UMLS) and WordNet, and our computer generated Concept Space, into one system. Our unique contribution results from combining the UMLS Semantic Net with Concept Space in our deep semantic parsing (DSP) algorithm. This algorithm establishes a medical query context based on the UMLS Semantic Net, which allows Concept Space terms to be filtered so as to isolate related terms relevant to the query. We performed two user studies in which Medical Concept Mapper terms were compared against human experts' terms. We conclude that the AZ Noun Phraser is well suited to extract medical phrases from user queries, that WordNet is not well suited to provide strictly medical synonyms, that the UMLS Metathesaurus is well suited to provide medical synonyms, and that Concept Space is well suited to provide related medical terms, especially when these terms are limited by our DSP algorithm.

Artificial Intelligence Lab, Department of MIS, University of Arizona / We propose a methodology to collect, quantify and visualize qualitative consumer data. We employ a Web-based Group Support System (GSS), GSw,b, to elicit free-form comments and a prototype comment analysis support system to facilitate comment classification, categorization and visualization to measure attitudes. We argue that such a methodology is needed due to the proliferation of qualitative data, the limitations of qualitative data analysis and the dearth of methods to measure attitudes contained within free-form comments. We conducted two experiments to compare our methodology with two long-established traditional methods, Likert scale evaluations and first-week box office sales records. We found that our methodology provides equivalent and superior affective and evaluative attitude information, compared to Likert scale ratings. We also found that comment analysis more accurately reflected actual first-week box office sales than did Likert scale ratings. Comment analysis with the prototype tool was seventy-five percent more efficient than manual coding. We designed the prototype to generate visualizations to make sense of multiple attitude dimensions through at-a-glance understanding and comparative presentation. The methodology we propose overcomes drawbacks often associated with qualitative data analysis and offers marketers and researchers a method to measure attitudes from free-form comments. The results indicate that qualitative data in the form of freeform comments may be quantified and visualized to provide meaningful attitude assessment. Finally, we present future research directions to enhance data collection and the comment analysis support system.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This research reports an algorithmic approach to the automatic generation of thesauri for electronic community systems. The techniques used included term filtering, automatic indexing, and cluster analysis. The testbed for our research was the Worm Community System, which contains a comprehensive library of specialized community data and literature, currently in use by molecular biologists who study the nematode worm C. elegans. The resulting worm thesaurus included 2709 researchers’ names, 798 gene names, 20 experimental methods, and 4302 subject descriptors. On average, each term had about 90 weighted neighboring terms indicating relevant concepts. The thesaurus was developed as an online search aide. We tested the worm thesaurus in an experiment with six worm researchers of varying degrees of expertise and background. The experiment showed that the thesaurus was an excellent “memory-jogging” device and that it supported learning and serendipitous browsing. Despite some occurrences of obvious noise, the system was useful in suggesting relevant concepts for the researchers’ queries and it helped improve concept recall. With a simple browsing interface, an automatic thesaurus can become a useful tool for online search and can assist researchers in exploring and traversing a dynamic and complex electronic community system.

Artificial Intelligence Lab, Department of MIS, University of Arizona / The basic building block of a multilingual information retrieval system is the input system. Chinese and Japanese characters pose great challenges for the conventional 101-key alphabet-based keyboard, because they are radical-based and number in the thousands. This paper reviews the development of various approaches and then presents a framework and working demonstrations of Chinese and Japanese input methods implemented in Java, which allow open deployment over the web to any platform, The demo includes both popular keyboard input methods and neural network handwriting recognition using a mouse or pen. This framework is able to accommodate future extension to other input mediums and languages of interest.

Artificial Intelligence Lab, Department of MIS, University of Arizona / As part of the ongoing Illinois Digital Library Initiative project, this paper presents the Java Search Agent Workshop (JSAW), a testbed designed for Java-based information searching. Based on artificial intelligence, neural networks, and G-Search, we implemented several search methods in Java to demonstrate their feasibility in various database, Internet, Intranet, and digital library search tasks. In addition to detailing our design rationale and implementation status, we present several sample Java implementations including a best first search spider and G-Search spider for Internet searching, and a Hopfield neural network based visualizer for database searching. Lessons learned and future directions are also presented.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper presents a neural network approach to document semantic indexing. A Hopfield net algorithm was used to simulate human associative memory for concept exploration in the domain of computer science and engineering. INSPEC, a collection of more than 320,000 document abstracts from leading journals, was used as the document testbed. Benchmark tests confirmed that three parameters (maximum number of activated nodes, E - maximum allowable error, and maximum number of iterations) were useful in positively influencing network convergence behavior without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests confirmed our expectation that the Hopfield net algorithm is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end-user vocabularies.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This paper describes a study to explore the integration of Group Support Systems (GSS) and Artificial Intelligence (AI) technology to provide solicitation, analytical, visualization and sense-making support for attitudes from large distributed marketing focus groups. The paper describes two experiments and the concomitant evolutionary design and development of an attitude analysis process and the MindMine Comment Analysis Tool. The analysis process circumvents many of the problems associated with traditional data gathering via closed-ended questionnaires and potentially biased interviews by providing support for online free response evaluative comments. MindMine allows teams of raters to analyze comments from any source, including electronic meetings, discussion groups or surveys, whether they are Web-based or same-place. The analysis results are then displayed as visualizations that enable the team quickly to make sense of attitudes reflected in the comment set, which we believe provide richer information and a more detailed understanding of attitudes.

Artificial Intelligence Lab, Department of MIS, University of Arizona / With the exponential growth of the Internet, information can be shared among government agencies more easily than before. However, this also poses some design issues and challenges. This article reports on our experience in building an infrastructure for information sharing and collaboration in the law enforcement domain. Based on our user requirement studies with the Tucson Police Department, three main design challenges are identified and discussed in details. Based on our findings, we propose an infrastructure to address these issues. The proposed design consists of three modules, namely (1) Security and Confidentiality Management Module, (2) Information Access and Monitoring Module, and (3) Collaboration Module. A prototype system will be deployed and tested at the Tucson Police Department. We anticipate that our studies can potentially provide useful insight to other digital government research projects.

Artificial Intelligence Lab, Department of MIS, University of Arizona / We report results of an investigation where thirty subjects were observed performing subject-based search in an online catalog system. The observations have revealed a range of misconceptions users have when performing subject-based search. We have developed a taxonomy that characterizes these misconceptions and a knowledge representation which explains these misconceptions. Directions for improving search performance are also suggested.

Artificial Intelligence Lab, Department of MIS, University of Arizona / This article describes research in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. This research builds upon previous work in automating the meeting classification process using a Hopfield neural network. Evaluation of the Kohonen output comparing it with Hopfield and human expert output using the same set of data found that the Kohonen SOM performed as well as a human expert in representing term association in the meeting output and outperformed the Hopfield neural network algorithm. Recall of consensus meeting concepts and topics using the Kohonen algorithm was equivalent to that of the human expert.