Semantic definition and matching for implementing national spatial data infrastructures. Issue 2 (2nd July 2016)

• Record Type: Journal Article
• Authors: Ulutaş, Deniztan; Kara, Gülten; Cömert, Çetin
• Is Part Of: Journal of spatial science. Volume 61:Issue 2(2016)
• Page Start: 441
• Page End: 459
• Publication Date: 2016-07-02
• Subjects: Semantic matching -- semantic definition -- ontology -- SDI -- semantic annotation; Geographic information systems -- Periodicals; Cartography -- Periodicals; Surveying -- Periodicals; Geodesy -- Periodicals; Photogrammetry -- Periodicals; Cartography; Geodesy; Geographic information systems; Photogrammetry; Surveying; Periodicals; 526.05
• DOI: 10.1080/14498596.2016.1142397
• Languages: English
• ISSNs: 1449-8596

Abstract

Abstract: In Turkey, the establishment of National Spatial Data Infrastructure (NSDI) is on the agenda. The technologies which are still in use for the technological infrastructure of any SDI are 'syntactic web' technologies. However, it is foreseen that in the near future, the current technologies will be replaced by 'semantic web' (SW) technologies. Being interoperability infrastructures, SDIs enable the integration of data from different sources. 'Semantic schema matching' (SSM) is a recent semantic web technology (SWT) for enabling data integration in SDIs in an 'automatic' manner. The main requirement of SSM is 'semantic data definition' (SDD). This has formed the motivation for this work, which aimed at developing a methodology for SDD for the participators in an NSDI and integration of their data ontologies, which we tried to match semantically. For this purpose, in our SSM scenario, we converted the road schema of the General Command of Mapping (GCM) and the road transport network (RTN) schema of INSPIRE (Infrastructure for Spatial Information in Europe) to SW languages using SWT. For semantic matching (SM), data ontologies were committed to the domain and upper ontologies. In this process, an ontology extension methodology was proposed and implemented. Then, we performed SM between these data ontologies with S-Match. S-Match finds semantic relations between ontologies using WordNet (WN) as a default background knowledgebase (BK), although it has quite a finite scope in geospatial information. In this study, we focused on this aspect and created a spatial-specific BK to obtain a sufficient amount of geographic information for performing and determining the requirements of SM. To this aim, a number of problems have been identified and it has been found that a fully automated schema matching by SWT is not possible today. As a result, it has been found that usage of a more specific source of background yields better results in a SM process.