Representation and Querying of Valid Time of Triples in Linked Geospatial Data

Bereta, Konstantina; Smeros, Panayiotis; Koubarakis, Manolis

doi:10.1007/978-3-642-38288-8_18

Cited by 48 publications

(48 citation statements)

References 17 publications

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“…Several extensions of the data model RDF and the query language stSPARQL were proposed in literature. The data model stRDF and the query language stSPARQL are extensions of RDF and SPARQL 1.1 respectively, developed for the representation and querying of spatial (Kyzirakos et al, 2012) and temporal data (i.e., the valid time of triples (Bereta et al, 2013)). More specifically, the data model stRDF proposes the representation of geometries as literals of the datatypes Well-known-text (WKT) and GML, that are OGC standards.…”

Section: Rdf and Sparqlmentioning

confidence: 99%

“…Other extensions of RDF and SPARQL were also proposed, such as the framework of stRDF and stSPARQL which extends RDF and SPARQL with both space and time features (Kyzirakos et al, 2010, Bereta et al, 2013. These standards of geospatial support have also been implemented in several RDF stores, such as Parliament 1 , uSeekM 2 , Virtuoso 3 , Stardog 4 and Strabon 5 (Kyzirakos et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Answering Geosparql Queries Over Relational Data

Bereta

Xiao

Koubarakis

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper we present the system Ontop-spatial that is able to answer GeoSPARQL queries on top of geospatial relational databases, performing on-the-fly GeoSPARQL-to-SQL translation using ontologies and mappings. GeoSPARQL is a geospatial extension of the query language SPARQL standardized by OGC for querying geospatial RDF data. Our approach goes beyond relational databases and covers all data that can have a relational structure even at the logical level. Our purpose is to enable GeoSPARQL querying on-the-fly integrating multiple geospatial sources, without converting and materializing original data as RDF and then storing them in a triple store. This approach is more suitable in the cases where original datasets are stored in large relational databases (or generally in files with relational structure) and/or get frequently updated.

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Section: Rdf and Sparqlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Answering Geosparql Queries Over Relational Data

Bereta

Xiao

Koubarakis

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In the former, the temporal information attached to a triple may either be an interval or a timestamp representing an instant. [3,6,7,10] The difference between interval and timestamp is not deeply relevant; with an interval representation, one can always simulate a timestamp by setting the start and end points of the interval to be identical, with no loss of information. For the purposes of this work, given the requirement to group sections of the data stream in order to implement what is needed for verification, it seems most appropriate to use the graph approach.…”

Section: Temporal Graphs On the Ethereum Distributed Ledgermentioning

confidence: 99%

Towards the Temporal Streaming of Graph Data on Distributed Ledgers

Third

Tiddi

Bastianelli

et al. 2017

Lecture Notes in Computer Science

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Abstract. We present our work-in-progress on handling temporal RDF graph data using the Ethereum distributed ledger. The motivation for this work are scenarios where multiple distributed consumers of streamed data may need or wish to verify that data has not been tampered with since it was generated -for example, if the data describes something which can be or has been sold, such as domestically-generated electricity. We describe a system in which temporal annotations, and information suitable to validate a given dataset, are stored on a distributed ledger, alongside the results of fixed SPARQL queries executed at the time of data storage. The model adopted implements a graph-based form of temporal RDF, in which time intervals are represented by named graphs corresponding to ledger entries. We conclude by discussing evaluation, what remains to be implemented, and future directions.

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“…New specifications, such as OWL-Time, have been developed [40,41]. Some space-time modeling works applying these standards can be found in [42][43][44][45]. For instance, Bereta and colleagues developed a new query language stSPARQL (space-time semantic web query language) to support expressive encoding of time in OWL ontologies [43].…”

Section: Progress In Developing a Spatiotemporal Reasoning Platformmentioning

confidence: 99%

“…Some space-time modeling works applying these standards can be found in [42][43][44][45]. For instance, Bereta and colleagues developed a new query language stSPARQL (space-time semantic web query language) to support expressive encoding of time in OWL ontologies [43]. Huang and Deng [44] pointed out the importance of enabling spatio-temporal reasoning based on geo-ontology and SWRL (Semantic Web Rule Language); however, they only touched very briefly and conceptually on the solution.…”

Section: Progress In Developing a Spatiotemporal Reasoning Platformmentioning

confidence: 99%

An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

Zhou

2016

IJGI

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Abstract:Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.

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Representation and Querying of Valid Time of Triples in Linked Geospatial Data

Cited by 48 publications

References 17 publications

Answering Geosparql Queries Over Relational Data

Answering Geosparql Queries Over Relational Data

Towards the Temporal Streaming of Graph Data on Distributed Ledgers

An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

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