CHOROS 2: Improving the Performance of Qualitative Spatial Reasoning in OWL

Mainas, Nikolaos; Petrakis, Euripides G. M.

doi:10.1109/ictai.2014.50

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…We worked with the first 1,000 data set records referring to positions of 20 ships at various time instances. The CHRONOS [2] and CHOROS [34] reasoners are applied for inferring all implied spatiotemporal relations (including spatial relations between any two ships) and checking the ontology for consistency. All inferred relations are instantiated in the ontology, resulting in an ontology with 2,113,835 triples.…”

Section: Discussionmentioning

confidence: 99%

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SOWL QL: Querying Spatio-Temporal Ontologies in OWL

et al. 2016

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We introduce SOWL QL, a query language for spatio-temporal information in ontologies. Building-upon SOWL (Spatio-Temporal OWL), an ontology for handling spatio-temporal information in OWL, SOWL QL supports querying over qualitative spatio-temporal information (expressed using natural language expressions such as "before", "after", "north of", "south of") rather than merely quantitative information (exact dates, times, locations). SOWL QL extends SPARQL with a powerful set of temporal and spatial operators, including temporal Allen topological, spatial directional and topological operations or combinations of the above. SOWL QL maintains simplicity of expression, and also upward and downward compatibility with SPARQL. Query translation in SOWL QL yields SPARQL queries, implying that querying spatio-temporal ontologies using SPARQL is still feasible but suffers from several drawbacks, the most important of them being that, queries in SPARQL become particularly complicated and users must be familiar with the underlying spatio-temporal representation (the "N-ary relations" or the "4D-fluents" approach in this work). Finally, querying in SOWL QL is supported by the SOWL reasoner which is not part of the standard SPARQL translation. The run-time performance of SOWL QL has been assessed experimentally in a real data setting. A critical analysis of its performance is also presented.B Euripides G. M. Petrakis

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Section: Discussionmentioning

confidence: 99%

“…To deal with the problem of efficiency, reasoning in SOWL QL resorts to CHOROS [34] and CHRONOS [2] reasoners for temporal and spatial information, respectively. CHOROS is a dedicated spatial reasoner for directional CSD-9 or RCC-8 relations implemented in Java.…”

Section: Reasoning In Sowl Qlmentioning

confidence: 99%

SOWL QL: Querying Spatio-Temporal Ontologies in OWL

et al. 2016

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“…On the other hand, reasoning systems, such as Pelletspatial (Stocker and Sirin, 2009), CHOROS (Christodoulou et al, 2012) or its next version CHOROS 2.0 (Mainas et al, 2014) improving the run-time performance, extract spatial relations from a knowledge base and reason over both these topological and directional relations. However, reasoning through specialised software also complicates the system reusability and data sharing (Batsakis and Antoniou, 2014).…”

Section: Spatial Reasoningmentioning

confidence: 99%

Towards a semantic framework for exploiting heterogeneous environmental data

Tran

Bouju

Plumejeaud-Perreau

et al. 2016

IJMSO

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This paper presents the Environment and landscape geo-knowledge (GEMINAT) project which aims to build an infrastructure favouring the cross-analysis of spatio-temporal heterogeneous data sources recorded at the Chizé environmental observatory since 1994. From a case study, we summarise the difficulties encountered by biologists and ecologists when maintaining and analysing collected environmental data, essentially the spatial organisation of the landscape, crop rotation, and wildlife data. We show how a framework which uses a spatiotemporal ontology as a semantic mediator can solve challenges related to the analysis and maintenance of these heterogeneous data.

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