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Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process

Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process

Jayanthi Ganapathy, Uma V.
Copyright: © 2019 |Volume: 15 |Issue: 2 |Pages: 22
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522564331|DOI: 10.4018/IJIIT.2019040103
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MLA

Ganapathy, Jayanthi, and Uma V. "Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process." IJIIT vol.15, no.2 2019: pp.32-53. http://doi.org/10.4018/IJIIT.2019040103

APA

Ganapathy, J. & V., U. (2019). Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process. International Journal of Intelligent Information Technologies (IJIIT), 15(2), 32-53. http://doi.org/10.4018/IJIIT.2019040103

Chicago

Ganapathy, Jayanthi, and Uma V. "Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process," International Journal of Intelligent Information Technologies (IJIIT) 15, no.2: 32-53. http://doi.org/10.4018/IJIIT.2019040103

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Abstract

Knowledge discovery with geo-spatial information processing is of prime importance in geomorphology. The temporal characteristics of evolving geographic features result in geo-spatial events that occur at a specific geographic location. Those events when consecutively occur result in a geo-spatial process that causes a phenomenal change over the period of time. Event and process are essential constituents in geo-spatial dynamism. The geo-spatial data acquired by remote sensing technology is the source of input for knowledge discovery of geographic features. This article performs qualitative inference of geographic process by identifying events causing geo-spatial deformation over time. The evolving geographic features and their types have association with spatial and temporal factors. Event calculus-based spatial knowledge formalism allows reasoning over intervals of time. Hence, representation of Event Attributed Spatial Entity (EASE) Knowledge is proposed. Logical event-based queries are evaluated on the formal representation of EASE Knowledge Base. Event-based queries are executed on the proposed knowledge base and when experimented on, real data sets yielded comprehensive results. Further, the significance of EASE-based spatio-temporal reasoning is proved by evaluating with respect to query processing time and accuracy. The enhancement of EASE with a direction for further development to explore its significance towards prediction is discussed towards the end.

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