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Geometry and geospatial data on the web
Published in Pieter Pauwels, Kris McGlinn, Buildings and Semantics, 2023
Anna Wagner, Mathias Bonduel, Jeroen Werbrouck, Kris McGlinn
The geographic query language for RDF data GeoSPARQL v1.0 [303] is standardised by the OGC and aims to provide RDF structures to describe geospatial data and include their geometry descriptions as RDF literals. GeoSPARQL includes a relatively simple high-level vocabulary for describing geospatial data and an extension to the SPARQL query language for processing that data. The main classes are SpatialObject and Feature, covering both spatial representations and features respectively (see Figure 4.19). Geometry then enables the representation of geometric objects, serialisable as GML and WKT. The query language’s documentation includes the expected structure of the considered geometry descriptions. Its main restriction on geometry descriptions is that only the spatial querying of two-dimensional geometry is considered in GeoSPARQL v1.016, although there are proposed extensions to GeoSPARQL which will specifically address the need to support querying of 3D geometries [2].
Big Geospatial Data and the Geospatial Semantic Web: Current State and Future Opportunities
Published in Yulei Wu, Fei Hu, Geyong Min, Albert Y. Zomaya, Big Data and Computational Intelligence in Networking, 2017
Chuanrong Zhang, Weidong Li, Tian Zhao
The GeoSPARQL protocol was proposed by the OGC as an extension of SPARQL for querying geographic RDF data. GeoSPARQL queries are dominated by spatial join operations due to the fine-grained nature of the RDF data model. Lack of spatial indices causes additional performance problems for GeoSPARQL queries. One reason for the poor performance problems is caused by the way that spatial attributes are stored in RDF data sets. Spatial attributes are usually stored as string literals that conform to certain formats such as WKT or GML. The GeoSPARQL query engine that implements spatial operators and filter functions has to parse these strings to recover the spatial coordinates for spatial computation. A nave implementation of a spatial operator or a filter function in GeoSPARQL treats its spatial inputs as plain strings and has to parse the strings to retrieve spatial contents such as x and y coordinates. Repeated parsing of the spatial inputs imposes a very large runtime overhead. The second reason for the poor performance problems is due to the lack of parallelization. Since spatial objects are not indexed, a GeoSPARQL query engine cannot partition ontology data into subsets to be processed in parallel. As a result, a GeoSPARQL query can only be processed as a single-threaded program. Even with precomputed spatial indices, partitioning spatial ontology data is not easy since the targeted data may not be evenly distributed in the indices.
Querying linked building data using SPARQL with functional extensions
Published in Symeon E. Christodoulou, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2017
To address this issue, we use SPARQL as a base query language and propose to extend a set of functions specific to the AEC domain. This strategy has been taken by other fields. For example, Open Geospatial Consortium (OGC) has released GeoSPARQL as a set of extended functions for geospatial data. It makes sense that the same approach can be taken for the building industry.
Towards knowledge-based geovisualisation using Semantic Web technologies: a knowledge representation approach coupling ontologies and rules
Published in International Journal of Digital Earth, 2020
In Europe, draft guidelines and vocabularies for representing INSPIRE geospatial data in RDF have been proposed, and most of the vocabularies are compatible with the GeoSPARQL query language through the reuse of certain predicates and subclass inheritance. In this study, we adopt the INSPIRE vocabularies that concern 2D buildings to represent geospatial data as linked data, and we showcase our approach through the visualisation of geospatial building data (cf. Section 4). Specifically, we mainly reuse the bu-base7 and bu-core2D8 vocabularies (cf. Figure 1).
WMO: an ontology for the semantic enrichment of wetland monitoring data
Published in International Journal of Digital Earth, 2023
Xin Xiao, Hui Lin, Chaoyang Fang
The adoption of semantic technologies in geoscience has been an active research area in recent years (Ma 2022). In the geoscience context, the semantic representation of geospatial information is the focus of this type of research. The GeoSPARQL (Perry and Herring 2023), proposed by the open geospatial consortium, is a standardized implementation for representing and querying geospatial data. GeoSPARQL provides a set of classes and properties for representing discrete vector geospatial data, such as points, lines, and polygons. A typical case is the application of GeoSPARQL to urban ontology or urban knowledge graph construction (Kuster, Hippolyte, and Rezgui 2020), which can well describe urban objects, such as traffic lights (points), roads (lines), and green spaces (polygons). However, in the context of wetland monitoring, we focus on a continuous geographical space, which cannot be simply expressed by points, lines, and polygons. Therefore, a majority of wetland monitoring data cannot be integrated into a unified geospatial reference with the current architecture of GeoSPARQL. Fortunately, the DGGS provides a potential solution, which may provide a more flexible representation of space. In the field of digital Earth, the DGGS is a framework for dividing the surface of the Earth into a nonoverlapping grid of cells based on the concept of tessellation, which is proposed as an effective way to integrate disparate geospatial data into a common spatial reference system (Li and Stefanakis 2020; Hojati et al. 2022). In another perspective, DGGS is stratified to divide Earth’s surface, so that is its ability to handle monitoring data at different resolutions. Based on this, the DGGS can group continuous wetland monitoring data of varying scale into discrete spatial objects by subdividing the wetland surface into a limited number of discrete regions (Rawson, Sabeur, and Brito 2022).