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In this paper we consider how Qualitative Spatial Reasoning (QSR) can be used to answer queries over large-scale knowledge graphs such as YAGO and DBPedia. We describe the challenges associated with spatially querying knowledge graphs such as point based representations, sparsity of qualitative relations, and scale. We address these challenges and present a query engine, Parallel Qualitative Reasoner-Query Engine (ParQR-QE), that uses a novel distributed qualitative spatial reasoning algorithm to provide answers to GeoSPARQL queries. An experimental evaluation using a range of different query types and the YAGO knowledge graph shows the advantages of QSR techniques in comparison to purely quantitative approaches. • Demonstration of techniques to develop enhanced large-scale knowledge graphs. • Integration of qualitative spatial reasoning into query answering. • Development of a distributed spatial query answering system for large-scale knowledge graphs. • Evaluation shows advantages of querying using qualitative spatial reasoning. [ABSTRACT FROM AUTHOR]

The INSPIRE Directive by the European Commission sets the legal and technical foundations towards interoperable Spatial Data Infrastructures (SDIs) across Europe. EU member states are already providing such services for several geospatial data themes (e.g., transportation networks, administrative units). Unfortunately, the INSPIRE ecosystem has been largely disjoint from the Semantic Web, without any means to repurpose existing SDIs as high-quality data sources, and thus multiply their value through interlinking, reasoning and inferencing. In this paper, we introduce a methodology that can assist stakeholders in exposing INSPIRE-aligned SDIs on the Semantic Web according to the recent GeoSPARQL standard. We develop methods for discovering INSPIRE data through a virtual SPARQL endpoint over existing INSPIRE catalogue services. Further, we implement a suite of tools for automatically transforming INSPIRE data and metadata into RDF triples with geometries. The compiled geographic and thematic information can then be loaded into semantic repositories for querying or interlinked with other data. Our open-source solutions essentially repurpose existing INSPIRE SDIs, so as to promote uptake and facilitate their reuse in practice. Finally, as a case study, we report our experience in validating this approach on a real-world SDI with publicly available data for Greece in order to expose its contents through (Geo)SPARQL endpoints. [ABSTRACT FROM AUTHOR]

This paper presents the design of a GeoSPARQL query processing solution for scientific raster array data, called GeoLD. The solution enables the implementation of SPARQL endpoints on top of OGC standard Web Coverage Processing Services (WCPS). Thus, the semantic querying of scientific raster data is supported without the need of specific raster array functions in the language. To achieve this, first Coverage to RDF mapping solutions were defined, based on the well-known W3C standard mappings for relational data. Next, the SPARQL algebra is extended with a new operator that delegates part of the GeoSPARQL query in WCPS services. Query optimization replaces those parts of the SPARQL query plan that may be delegated to a WCPS service by instances of such new WCPS operator. A first prototype has been implemented by extending the ARQ SPARQL query engine of Apache Jena. Petascope was used as the WCPS implementation on top of the Rasdaman raster array database. An initial evaluation with real meteorological data shows, as it was initially expected, that the approach outperforms an existing reference relational database based GeoSPARQL implementation. • Efficient support for GeoSPARQL queries on top of raster coverages. • Definition of raster coverage to RDF mapping approaches. • Federated vector–raster geospatial queries enabled with SPARQL. • Raster data access enabled without additional specific operators beyond GeoSPARQL. [ABSTRACT FROM AUTHOR]

With the advancement of Semantic Technologies, large geospatial data sources have been increasingly published as Linked data on the Web. The LinkedGeoData project is one of the most prominent such projects to create a large knowledge graph from OpenStreetMap (OSM) with global coverage and interlinking of other data sources. In this paper, we report on the ongoing effort of exposing the relational database in LinkedGeoData as a SPARQL endpoint using Virtual Knowledge Graph (VKG) technology. Specifically, we present two realizations of VKGs, using the two systems Sparqlify and Ontop. In order to improve compliance with the OGC GeoSPARQL standard, we have implemented GeoSPARQL support in Ontop v4. Moreover, we have evaluated the VKG-powered LinkedGeoData in the test areas of Italy and Germany. Our experiments demonstrate that such system supports complex GeoSPARQL queries, which confirms that query answering in the VKG approach is efficient. [ABSTRACT FROM AUTHOR]

In this paper, we propose an OBDA approach for accessing geospatial data stored in relational databases using the R2RML mappings and OGC standard GeoSPARQL. On the theoretical side, we provide a formalization of GeoSPARQL in terms of SPARQL entailment regime. For a practical query answering algorithm, we introduce an extension to the existing SPARQL-to-SQL translation method to support GeoSPARQL features. Our approach has been implemented in the system Ontop-spatial, an extension of the OBDA system Ontop for creating virtual geospatial RDF graphs on top of geospatial relational databases. We present an experimental evaluation of our system using and extending a state-of-the-art benchmark. To measure the performance of our system, we compare it to two state-of-the-art geospatial RDF stores –a free and open-source one, and a commercial one– and confirm its efficiency. [ABSTRACT FROM AUTHOR]