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dc.contributor.authorChen, Hsinchun
dc.contributor.authorSmith, Terrence R.
dc.contributor.authorLarsgaard, Mary L.
dc.contributor.authorHill, Linda L.
dc.contributor.authorRamsey, Marshall C.
dc.date.accessioned2004-09-20T00:00:01Z
dc.date.available2010-06-18T23:27:16Z
dc.date.issued1997-09en_US
dc.date.submitted2004-09-20en_US
dc.identifier.citationA Geographical Knowledge Representation System (GKRS)for Multimedia Geospatial Retrieval and Analysis 1997-09, 1(2):132-152 International Journal of Digital Librariesen_US
dc.identifier.urihttp://hdl.handle.net/10150/105551
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractDigital libraries serving multimedia information that may be accessed in terms of geographic content and relationships are creating special challenges and opportunities for networked information systems. An especially challenging research issue concerning collections of geo-referenced information relates to the development of techniques supporting geographic information retrieval (GIR) that is both fuzzy and concept-based. Viewing the meta-information environment of a digital library as a heterogeneous set of services that support users in terms of GIR, we define a geographic knowledge representation system (GKRS) in terms of a core set of services of the meta-information environment that is required in supporting concept-based access to collections of geospatial information. In this paper, we describe an architecture for a GKRS and its implementation in terms of a prototype system. Our GKRS architecture loosely couples a variety of multimedia knowledge sources that are in part represented in terms of the semantic network and neural network representations developed in artificial intelligence research. Both textual analysis and image processing techniques are employed in creating these textual and iconic geographcal knowledge structures. The GKRS also employs spreading activation algorithms in support of concept-based knowledge retrieval. The- paper describes implementational details of several of the components of the GKRS as well as discussing both the lessons learned from, and future directions of, our research.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherSpringer-Verlagen_US
dc.subjectEvaluationen_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.subject.otherGeographic Knowledge Representation System (GKRS)en_US
dc.titleA Geographical Knowledge Representation System (GKRS)for Multimedia Geospatial Retrieval and Analysisen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalInternational Journal of Digital Librariesen_US
refterms.dateFOA2018-08-21T12:49:15Z
html.description.abstractDigital libraries serving multimedia information that may be accessed in terms of geographic content and relationships are creating special challenges and opportunities for networked information systems. An especially challenging research issue concerning collections of geo-referenced information relates to the development of techniques supporting geographic information retrieval (GIR) that is both fuzzy and concept-based. Viewing the meta-information environment of a digital library as a heterogeneous set of services that support users in terms of GIR, we define a geographic knowledge representation system (GKRS) in terms of a core set of services of the meta-information environment that is required in supporting concept-based access to collections of geospatial information. In this paper, we describe an architecture for a GKRS and its implementation in terms of a prototype system. Our GKRS architecture loosely couples a variety of multimedia knowledge sources that are in part represented in terms of the semantic network and neural network representations developed in artificial intelligence research. Both textual analysis and image processing techniques are employed in creating these textual and iconic geographcal knowledge structures. The GKRS also employs spreading activation algorithms in support of concept-based knowledge retrieval. The- paper describes implementational details of several of the components of the GKRS as well as discussing both the lessons learned from, and future directions of, our research.


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