9 December 2015 A VGI data integration framework based on linked data model
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Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 98082M (2015) https://doi.org/10.1117/12.2211068
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China
Abstract
This paper aims at the geographic data integration and sharing method for multiple online VGI data sets. We propose a semantic-enabled framework for online VGI sources cooperative application environment to solve a target class of geospatial problems. Based on linked data technologies – which is one of core components of semantic web, we can construct the relationship link among geographic features distributed in diverse VGI platform by using linked data modeling methods, then deploy these semantic-enabled entities on the web, and eventually form an interconnected geographic data network to support geospatial information cooperative application across multiple VGI data sources. The mapping and transformation from VGI sources to RDF linked data model is presented to guarantee the unique data represent model among different online social geographic data sources. We propose a mixed strategy which combined spatial distance similarity and feature name attribute similarity as the measure standard to compare and match different geographic features in various VGI data sets. And our work focuses on how to apply Markov logic networks to achieve interlinks of the same linked data in different VGI-based linked data sets. In our method, the automatic generating method of co-reference object identification model according to geographic linked data is discussed in more detail. It finally built a huge geographic linked data network across loosely-coupled VGI web sites. The results of the experiment built on our framework and the evaluation of our method shows the framework is reasonable and practicable.
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Lin Wan, Lin Wan, Rongrong Ren, Rongrong Ren, } "A VGI data integration framework based on linked data model", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98082M (9 December 2015); doi: 10.1117/12.2211068; https://doi.org/10.1117/12.2211068
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