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METHODOLOGY OF THE DEVELOPMENT OF THE DATA MODEL FOR SPATIAL LINKED DATA WITH POINT GEOMETRY

Otakar Čerba

First published: 2018-06-20https://doi.org/10.5593/sgem2018/2.2/s08.038View metrics

Abstract

Linked Open Data or Linked Data is an innovative approach to data management regarding spatial and geographic data as well. Its main benefits consist in using existing web technologies, including Uniform Resource Identifier as feature identifier and links connecting particular data objects. This paper discusses a methodology of the development of a data model respecting the Linked Data approach for spatial data with point geometry (called points of interest). The complete development process is composed of several steps ? building of a core model with necessary properties, construction of an extended model (there is expedient to involve a broad group of potential users and domain experts in this phase), searching corresponding features in existing vocabularies, data models and data sets (to make the model more semantically interoperable), proposing of identity or topological links to other data objects published as Linked Data, testing and feedback retrieving and a determination of a way of requirements and changes recording as well as a plan for a publishing of data model modifications. These particular steps are illustrated by the construction of data model of the Smart Points of Interest data, which represents the one the largest points of interest data published as Linked Data. The goal of this paper is to provide a relevant directions how to advance Linked Data modeling in the geo- domain and to open a discussion about development of spatial Linked Open Data sets in general.

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Publication details

Title
METHODOLOGY OF THE DEVELOPMENT OF THE DATA MODEL FOR SPATIAL LINKED DATA WITH POINT GEOMETRY
Authors
Otakar Čerba
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2018
Pages
301-308
SWS Citekey
Cerba20188301308
ISSN
1314-2704
ISBN
978-619-7408-40-9
Language
en
Publication type
Conference Paper
Keywords
Spatial dataLinked DataData modellingSemanticsGeographical data
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