Persistent Link:
http://hdl.handle.net/10150/105613
Title:
Semantic Retrieval for the NCSA Mosaic
Author:
Chen, Hsinchun; Schatz, Bruce R.
Citation:
Semantic Retrieval for the NCSA Mosaic 1994,
Issue Date:
1994
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105613
Submitted date:
2004-10-01
Abstract:
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.
Type:
Conference Paper
Language:
en
Keywords:
Digital Libraries; 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.authorSchatz, Bruce R.en_US
dc.date.accessioned2004-10-01T00:00:01Z-
dc.date.available2010-06-18T23:28:19Z-
dc.date.issued1994en_US
dc.date.submitted2004-10-01en_US
dc.identifier.citationSemantic Retrieval for the NCSA Mosaic 1994,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105613-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractIn 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectDigital Librariesen_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 Retrieval for the NCSA Mosaicen_US
dc.typeConference Paperen_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.