Information science researchers have recently turned to new artificial intelligence-based inductive learning techniques including neural networks, symbolic learning and genetic algorithms. An overview of the new techniques and their usage in information science research is provided. The algorithms adopted for a hybrid genetic algorithms and neural nets based system, called GANNET, are presented. GANNET performed concept (keyword) optimization for user-selected documents during information retrieval using the genetic algorithms. It then used the optimized concepts to perform concept exploration in a large network of related concepts through the Hopfield net parallel relaxation procedure. Based on a test collection of about 3,000 articles from DIALOG and an automatically created thesaurus, and using Jaccard's score as a performance measure, the experiment showed that GANNET improved the Jaccard's scores by about 50% and helped identify the underlying concepts that best describe the user-selected documents.

In this paper we report an automatic and scalable concept space approach to enhancing the deep searching capability of the NCSA Mosaic. The research, which is based on the findings from a previous NSF National Collaboratory project and which will be expanded in a new Illinois NSF/ARPA/NASA Digital Library project, centers around semantic retrieval and user customization. Semantic retrieval supports a higher level of abstraction in user search, which can overcome the vocabulary problem for information retrieval. Rather than searching for words within the object space, the search is for terms within a concept space (graph of terms occurring within objects linked to each other by the frequency with which they occur together). Co-occurrence graphs seem to provide good suggestive power in specialized domains, such as biology. By providing a more understandable, system-generated, semantics-rich concept space as an abstraction of the enormously complex object space plus algorithms and interface to assist in object/concept spaces traversal, we believe we can greatly alleviate both information overload and the vocabulary problem of internet services. These techniques will also be used to provide a form of customized retrieval and automatic information routing. Results from past research, the specific algorithms and techniques, and the research plan for enhancing the NCSA Mosaic's search capability in the NSF/ARPA/NASA Digital Library project will be discussed.

This paper describes the development and testing of the Medical Concept Mapper as an aid to providing synonyms and semantically related concepts to improve searching. All terms are related to the userquery and fit into the query context. The system is unique because its five components combine humancreated and computer-generated elements. The Arizona Noun Phraser extracts phrases from natural language user queries. WordNet and the UMLS Metathesaurus provide synonyms. The Arizona Concept Space generates conceptually related terms. Semantic relationships between queries and concepts are established using the UMLS Semantic Net. Two user studies conducted to evaluate the system are described.

In this article, we report our implementation and comparison of two text clustering techniques. One is based on Wardâ s clustering and the other on Kohonenâ s Self-organizing Maps. We have evaluated how closely clusters produced by a computer resemble those created by human experts. We have also measured the time that it takes for an expert to â â clean upâ â the automatically produced clusters. The technique based on Wardâ s clustering was found to be more precise. Both techniques have worked equally well in detecting associations between text documents. We used text messages obtained from group brainstorming meetings.

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.

As new and emerging classes of information systems applications the applications become more overwhelming, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent in this “network-centric” information age. Information overload, a result of the ease of information creation and rendering via the Internet and the World Wide Web, has become more evident in people’s lives. Significant variations of database formats and structures, the richness of information media, and an abundance of multilingual information content also have created severe information interoperability problems-structural interoperability, media interoperability, and multilingual interoperability. The conventional approaches to addressing information overload and information interoperability problems are manual in nature, requiring human experts as information intermediaries to create knowledge structures and/or ontologies. As information content and collections become even larger and more dynamic, we believe a systemaided bottom-up artificial intelligence (AI) approach is needed. By applying scalable techniques developed in various AI subareas such as image segmentation and indexing, voice recognition, natural language processing, neural networks, machine learning, clustering and categorization, and intelligent agents, we can provide an alternative system-aided approach to addressing both information overload and information interoperability.

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.

This research presents preliminary results generated from the semantic retrieval research component of the Illinois Digital Library Initiative (DLI) project. Using a variation of the automatic thesaurus generation techniques, to which we refer as the concept space approach, we aimed to create graphs of domain-specific concepts (terms) and their weighted co-occurrence relationships for all major engineering domains. Merging these concept spaces and providing traversal paths across different concept spaces could potentially help alleviate the vocabulary (difference) problem evident in large-scale information retrieval. We have experimented previously with such a technique for a smaller molecular biology domain (Worm Community System, with 10+ MBs of document collection) with encouraging results.

This chapter will focus on digital libraries, starting with a discussion of the historical visionaries, definitions, driving forces and enabling technologies and some key research issues. Also discussed will be some of the US and international digital library projects and research initiatives. Some of the emerging techniques for building large-scale digital libraries, including semantic interoperability, will be described. Finally, the conclusion will offer some future directions for digital libraries.

Due to the recent explosion of information in the biomedical field, it is hard for a single researcher to review the complex network involving genes, proteins, and interactions. We are currently building GeneScene, a toolkit that will assist researchers in reviewing existing literature, and report on the first phase in our development effort: extracting the relevant information from medical abstracts. We are developing a medical parser that extracts information, fills basic prepositional-based templates, and combines the templates to capture the underlying sentence logic. We tested our parser on 50 unseen abstracts and found that it extracted 246 templates with a precision of 70%. In comparison with many other techniques, more information was extracted without sacrificing precision. Future improvement in precision will be achieved by correcting three categories of errors.