Artificial Intelligence Lab, Department of MIS, University of Arizona / Digital libraries with multimedia geographic content present special challenges and opportunities in today's networked information environment. One of the most challenging research issues for geospatial collections is to develop techniques to support fuzzy, concept-based, geographic information retrieval. Based on an artificial intelligence approach, this project presents a Geospatial Knowledge Representation System (GKRS) prototype that integrates multiple knowledge sources (textual, image, and numerical) to support concept-based geographic information retrieval. Based on semantic network and neural network representations, GKRS loosely couples different knowledge sources and adopts spreading activation algorithms for concept-based knowledge inferencing. Both textual analysis and image processing techniques have been employed to create textual and visual geographical knowledge structures. This paper suggests a framework for developing a complete GKRS-based system and describes in detail the prototype system that has been developed so far.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105172 |
| Date | 07 1900 |
| Creators | Zhu, Bin, Ramsey, Marshall C., Ng, Tobun Dorbin, Chen, Hsinchun, Schatz, Bruce R. |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (On-line/Unpaginated) |
Page generated in 0.0476 seconds