Ontop-spatial: Ontop of geospatial databases

Bereta, Konstantina; Xiao, Guohui; Koubarakis, Manolis

doi:10.1016/j.websem.2019.100514

Cited by 61 publications

(20 citation statements)

References 14 publications

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“…QUARK [65], GeoVAQA [66], and QUASAR [67] represent examples of QA systems in the geographical domain however they extract the information needed to formulate the answer from text documents. More recently, systems like [68][69][70][71] instead exploit the geographic information contained in well known knowledge bases like DBpedia, OpenStreetMap (https://www.openstreetmap.org/, accessed on 4 August 2021) and the GADM [72] dataset. Although working on geographic information, none of these systems allow to query ontologies containing such specific information about phenomena collected from EO satellite images.…”

Section: Semantic Image Retrievalmentioning

confidence: 99%

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

et al. 2021

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Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”.

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Section: Semantic Image Retrievalmentioning

confidence: 99%

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

et al. 2021

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“…The gridded structure of the data is preserved and can be queried using SciSPARQL. Recently, the Ontop-spatial extension [18] can process raster data and create virtual geospatial RDF views above them. The tool automatically translates each raster pixel into a feature thanks to mapping declaration and the polygon dumping function of PostGIS.…”

Section: Processing Of Raster Data In a Semantic Frameworkmentioning

confidence: 99%

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

Tran

Aussenac-Gilles

Comparot

et al. 2020

IJGI

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Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculated from rasters as a way of qualifying geographic units through their spatio-temporal features. We propose (i) a modular ontology that contributes to the semantic and homogeneous description of spatio-temporal data to qualify predefined areas; (ii) a Semantic Extraction, Transformation, and Load (ETL) process, allowing us to extract data from rasters and to link them to the corresponding spatio-temporal units and features; and (iii) a resulting dataset that is published as an RDF triplestore, exposed through a SPARQL endpoint, and exploited by a semantic interface. We illustrate the integration process with raster files providing the land cover of a specific French winery geographic area, its administrative units, and their land registers over different periods. The results have been evaluated with regards to three use-cases exploiting these EO data: integration of time series observations; EO process guidance; and data cross-comparison.

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“…OBDI systems implementing this paradigm include Mastro (http://www.obdasystems.com/it/mastro/) [30], Morph (https://github.com/oeg-upm/morphrdb/) [31], Ontop [10], Stardog (https://www.stardog.com/), and Ultrawrap (https://capsenta.com/ ultrawrap/) [32]. Recently, Ontop has been extended to support GeoSPARQL [33]. Although not using the R2RML and OWL standards, the LinkedGeoData project [34] is a pioneer work which follows the principle of OBDI and converts the OpenStreetMap (OSM) data to an RDF graph and interlinks these data with other open RDF knowledge bases.…”

Section: Ontology-based Geospatial Data Integrationmentioning

confidence: 99%

A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics

Ding

Xiao

Calvanese

et al. 2020

IJGI

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In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making sense out of the combined data via sophisticated analysis methods. To address this challenge we rely on two well-established research areas: data integration and geovisual analytics, and propose to adopt an ontology-based approach to decouple the challenges of data access and analytics. Our framework consists of two modules centered around an ontology: (1) an ontology-based data integration (OBDI) module, in which mappings specify the relationship between the underlying data and a domain ontology; (2) a geovisual analytics (GeoVA) module, designed for the exploration of the integrated data, by explicitly making use of standard ontologies. In this framework, ontologies play a central role by providing a coherent view over the heterogeneous data, and by acting as a mediator for visual analysis tasks. We test our framework in a scenario for the investigation of the spatiotemporal patterns of meteorological and traffic data from several open data sources. Initial studies show that our approach is feasible for the exploration and understanding of heterogeneous geospatial data.

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Ontop-spatial: Ontop of geospatial databases

Cited by 61 publications

References 14 publications

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Semantic Integration of Raster Data for Earth Observation: An RDF Dataset of Territorial Unit Versions with their Land Cover

A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics

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