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    Semantic Issues for Digital Libraries

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    Author
    Chen, Hsinchun
    Editors
    Harum, S.
    Twindale, M.
    Issue Date
    2000
    Submitted date
    2004-10-01
    Keywords
    Artificial Intelligence
    Information Seeking Behaviors
    Information Extraction
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial Intelligence lab
    AI lab
    Information retrieval
    
    Metadata
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    Citation
    Semantic Issues for Digital Libraries 2000, :70-79 Successes and Failures of Digital Libraries, 35 Annual Clinic on Library Applications of Data Processing
    Publisher
    UIUC
    Journal
    Successes and Failures of Digital Libraries, 35 Annual Clinic on Library Applications of Data Processing
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105127
    Abstract
    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.
    Type
    Book Chapter
    Language
    en
    Collections
    DLIST

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