Collective Keyword Query on a Spatial Knowledge Base

Xiongnan Jin; Sangjin Shin; Eunju Jo; Kyong-Ho Lee

doi:10.1109/tkde.2018.2873376

Collective Keyword Query on a Spatial Knowledge Base

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2018.2873376 ◽

2019 ◽

Vol 31 (11) ◽

pp. 2051-2062 ◽

Cited By ~ 5

Author(s):

Xiongnan Jin ◽

Sangjin Shin ◽

Eunju Jo ◽

Kyong-Ho Lee

Keyword(s):

Knowledge Base ◽

Spatial Knowledge ◽

Keyword Query

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Development of a spatial knowledge base system for environment protection: the ARSEN project

Safety Science ◽

10.1016/0925-7535(94)00064-a ◽

1995 ◽

Vol 20 (1) ◽

pp. 27-37 ◽

Cited By ~ 1

Author(s):

Laurent Buisson ◽

Vincent Cligniez

Keyword(s):

Knowledge Base ◽

Spatial Knowledge ◽

Base System ◽

Environment Protection ◽

Knowledge Base System

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

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2019040103 ◽

2019 ◽

Vol 15 (2) ◽

pp. 32-53 ◽

Cited By ~ 1

Author(s):

Jayanthi Ganapathy ◽

Uma V.

Keyword(s):

Knowledge Base ◽

Knowledge Discovery ◽

Spatial Data ◽

Spatial Information ◽

Spatial Knowledge ◽

Spatial Process ◽

Formal Representation ◽

Geographic Process ◽

Event Based ◽

Spatial Entity

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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