An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs
The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/9/375 |
| _version_ | 1797579792532373504 |
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| author | Younes Hamdani Guohui Xiao Linfang Ding Diego Calvanese |
| author_facet | Younes Hamdani Guohui Xiao Linfang Ding Diego Calvanese |
| author_sort | Younes Hamdani |
| collection | DOAJ |
| description | The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works. |
| first_indexed | 2024-03-10T22:40:57Z |
| format | Article |
| id | doaj.art-167743ba5f6444568f8307e4efa2177c |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T22:40:57Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-167743ba5f6444568f8307e4efa2177c2023-11-19T11:01:11ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-09-0112937510.3390/ijgi12090375An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge GraphsYounes Hamdani0Guohui Xiao1Linfang Ding2Diego Calvanese3Department of Computing Science, Umeå University, 901 87 Umeå, SwedenDepartment of Information Science and Media Studies, University of Bergen, 5007 Bergen, NorwayDepartment of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7491 Trondheim, NorwayDepartment of Computing Science, Umeå University, 901 87 Umeå, SwedenThe integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works.https://www.mdpi.com/2220-9964/12/9/375raster data cubeontologyvirtual knowledge graphsgeospatial data integrationknowledge queryingSPARQL |
| spellingShingle | Younes Hamdani Guohui Xiao Linfang Ding Diego Calvanese An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs ISPRS International Journal of Geo-Information raster data cube ontology virtual knowledge graphs geospatial data integration knowledge querying SPARQL |
| title | An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs |
| title_full | An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs |
| title_fullStr | An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs |
| title_full_unstemmed | An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs |
| title_short | An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs |
| title_sort | ontology based framework for geospatial integration and querying of raster data cube using virtual knowledge graphs |
| topic | raster data cube ontology virtual knowledge graphs geospatial data integration knowledge querying SPARQL |
| url | https://www.mdpi.com/2220-9964/12/9/375 |
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