Persistent Link:
http://hdl.handle.net/10150/106303
Title:
Semantic Issues for Digital Libraries
Author:
Chen, Hsinchun
Citation:
Semantic Issues for Digital Libraries 2000, :51-60 Annual Clinic on Library Applications of Data Processing
Publisher:
UIUC
Journal:
Annual Clinic on Library Applications of Data Processing
Issue Date:
2000
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/106303
Submitted date:
2004-10-01
Abstract:
In this era of the Internet and distributed multimedia computing, new and emerging classes of information systems applications have swept into the lives of office workers and everyday people. New applications ranging from digital libraries, multimedia systems, geographic information systems, collaborative computing to electronic commerce, virtual reality, and electronic video arts and games have created tremendous opportunities for information and computer science researchers and practitioners. As the applications become more overwhelming, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent in this â networkcentricâ 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 peopleâ 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 system-aided 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
Keywords:
Evaluation; Information Seeking Behaviors; Digital Libraries
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-10-01T00:00:01Z-
dc.date.available2010-06-18T23:44:07Z-
dc.date.issued2000en_US
dc.date.submitted2004-10-01en_US
dc.identifier.citationSemantic Issues for Digital Libraries 2000, :51-60 Annual Clinic on Library Applications of Data Processingen_US
dc.identifier.urihttp://hdl.handle.net/10150/106303-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractIn this era of the Internet and distributed multimedia computing, new and emerging classes of information systems applications have swept into the lives of office workers and everyday people. New applications ranging from digital libraries, multimedia systems, geographic information systems, collaborative computing to electronic commerce, virtual reality, and electronic video arts and games have created tremendous opportunities for information and computer science researchers and practitioners. As the applications become more overwhelming, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent in this â networkcentricâ 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 peopleâ 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 system-aided 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherUIUCen_US
dc.subjectEvaluationen_US
dc.subjectInformation Seeking Behaviorsen_US
dc.subjectDigital Librariesen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.titleSemantic Issues for Digital Librariesen_US
dc.typeBook Chapteren_US
dc.identifier.journalAnnual Clinic on Library Applications of Data Processingen_US
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