Persistent Link:
http://hdl.handle.net/10150/105127
Title:
Semantic Issues for Digital Libraries
Author:
Chen, Hsinchun
Editors:
Harum, S.; Twindale, M.
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
Issue Date:
2000
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105127
Submitted date:
2004-10-01
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
Keywords:
Artificial Intelligence; Information Seeking Behaviors; Information Extraction
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Information retrieval

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.editorHarum, S.en_US
dc.contributor.editorTwindale, M.en_US
dc.date.accessioned2004-10-01T00:00:01Z-
dc.date.available2010-06-18T23:19:51Z-
dc.date.issued2000en_US
dc.date.submitted2004-10-01en_US
dc.identifier.citationSemantic Issues for Digital Libraries 2000, :70-79 Successes and Failures of Digital Libraries, 35 Annual Clinic on Library Applications of Data Processingen_US
dc.identifier.urihttp://hdl.handle.net/10150/105127-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractAs 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherUIUCen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectInformation Seeking Behaviorsen_US
dc.subjectInformation Extractionen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherInformation retrievalen_US
dc.titleSemantic Issues for Digital Librariesen_US
dc.typeBook Chapteren_US
dc.identifier.journalSuccesses and Failures of Digital Libraries, 35 Annual Clinic on Library Applications of Data Processingen_US
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