Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Entity resolution in geospatial data integration
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Interoperability for geospatial analysis: a semantics and ontology-based approach
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
Geospatial information integration based on the conceptualization of geographic domain
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools
Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools
An Integrative Approach to Geospatial Data Fusion
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
Improvement of spatial data quality using the data conflation
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
A data fusion system for spatial data mining, analysis and improvement
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
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Geospatial data integration technology involves fusing equivalent objects from multiple datasets. Due to different acquisition methods of geospatial data, new problem cases arise in data integration systems and thus the complexity of the integration approach increases. There are many data homogenization methods, which determine the assignment of objects via semantic similarity. The algorithms for polygonal geospatial data integration, presented in this paper, are based on geometrical comparison between two datasets. The objective of these algorithms is the assignment of geospatial elements representing the same object in heterogeneous datasets. Depending on semantic information in geospatial data the polygonal shapes have different spatial extent. For this reason two kinds of polygonal geospatial data were analyzed. The methods are discussed and first results are presented.