A generic methodology for geo‐related data semantic annotation. (4th May 2018)

• Record Type: Journal Article
• Authors: Malik, Kaleem Razzaq; Habib, Muhammad Asif; Khalid, Shehzad; Ahmad, Mudassar; Alfawair, Mai; Ahmad, Awais; Jeon, Gwanggil
• Other Names: Piccialli Francesco guestEditor.; Jung Jason J. guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 15(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-05-04
• Subjects: geo‐related data -- linked data -- resource description framework -- semantic annotation -- semantic web; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4495
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Geo‐related data, also known as spatial data, is represented using a vector used for representing longitude, elevation, and latitude. Specially built systems, well known as Geographical Information Systems (GIS), made use of such data for querying, manipulating, navigation, and analyzing. In the current era of data, science needs to involve smart interactive investigation involving Internet of Data (IoD) on predicting upcoming changes and spatial updates on the map is growing rapidly. To resolve issues concerning real‐time spatial data, transformation using semantic annotation can provide a better way to translate spatial relationships. These spatial relationships will support spatial analysis by linking different cause and effect with the help of reasoning mechanism. This research's major focus is on a data transformation methodology for geo‐related semantic annotation. Spatial dataset gets stored in a database and then transformed into Extensible Markup Language (XML) and Resource Description Framework (RDF). Even for bi‐directional transformation to work properly, we need to map different schema level transformations. A deep research is conducted to consider available mappings, implementations, and updates to further improving data fusion for having better compatibility. Then, transformed data as results get analyzed and discussed based on the data mapping rules formulated. It is aimed to show the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data.