SEMANTIC LINKING SPATIAL RDF DATA TO THE WEB DATA SOURCES

Abstract

Abstract. Large amounts of spatial data are hold in relational databases. Spatial data in the relational databases must be converted to RDF for semantic web applications. Spatial data is an important key factor for creating spatial RDF data. Linked Data is the most preferred way by users to publish and share data in the relational databases on the Web. In order to define the semantics of the data, links are provided to vocabularies (ontologies or other external web resources) that are common conceptualizations for a domain. Linking data of resource vocabulary with globally published concepts of domain resources combines different data sources and datasets, makes data more understandable, discoverable and usable, improves data interoperability and integration, provides automatic reasoning and prevents data duplication. The need to convert relational data to RDF is coming in sight due to semantic expressiveness of Semantic Web Technologies. One of the important key factors of Semantic Web is ontologies. Ontology means “explicit specification of a conceptualization”. The semantics of spatial data relies on ontologies. Linking of spatial data from relational databases to the web data sources is not an easy task for sharing machine-readable interlinked data on the Web. Tim Berners-Lee, the inventor of the World Wide Web and the advocate of Semantic Web and Linked Data, layed down the Linked Data design principles. Based on these rules, firstly, spatial data in the relational databases must be converted to RDF with the use of supporting tools. Secondly, spatial RDF data must be linked to upper level-domain ontologies and related web data sources. Thirdly, external data sources (ontologies and web data sources) must be determined and spatial RDF data must be linked related data sources. Finally, spatial linked data must be published on the web. The main contribution of this study is to determine requirements for finding RDF links and put forward the deficiencies for creating or publishing linked spatial data. To achieve this objective, this study researches existing approaches, conversion tools and web data sources for relational data conversion to the spatial RDF. In this paper, we have investigated current state of spatial RDF data, standards, open source platforms (particularly D2RQ, Geometry2RDF, TripleGeo, GeoTriples, Ontop, etc.) and the Web Data Sources. Moreover, the process of spatial data conversion to the RDF and how to link it to the web data sources is described. The implementation of linking spatial RDF data to the web data sources is demonstrated with an example use case. Road data has been linked to the one of the related popular web data sources, DBPedia. SILK, a tool for discovering relationships between data items within different Linked Data sources, is used as a link discovery framework. Also, we evaluated other link discovery tools e.g. LIMES, Silk and results are compared to carry out matching/linking task. As a result, linked road data is shared and represented as an information resource on the web and enriched with definitions of related different resources. By this way, road datasets are also linked by the related classes, individuals, spatial relations and properties they cover such as, construction date, road length, coordinates, etc.