Introduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective

Persistent Link:
http://hdl.handle.net/10150/105320
Title:
Introduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective
Author:
Chen, Hsinchun
Citation:
Introduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective 2003-05, 54(7):621-624 Journal of the American Society for Information Science & Technology
Publisher:
Wiley Periodicals, Inc
Journal:
Journal of the American Society for Information Science & Technology
Issue Date:
May-2003
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105320
Submitted date:
2004-08-16
Abstract:
Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information on the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Web Mining; World Wide Web
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Information retrieval; Machine learning

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-08-16T00:00:01Z-
dc.date.available2010-06-18T23:23:41Z-
dc.date.issued2003-05en_US
dc.date.submitted2004-08-16en_US
dc.identifier.citationIntroduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective 2003-05, 54(7):621-624 Journal of the American Society for Information Science & Technologyen_US
dc.identifier.urihttp://hdl.handle.net/10150/105320-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractResearch in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information on the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectWeb Miningen_US
dc.subjectWorld Wide Weben_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.subject.otherMachine learningen_US
dc.titleIntroduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspectiveen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Science & Technologyen_US
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