Ontology-based access to temporal data with Ontop: A framework proposal

• Autorzy: Güzel Kalaycı Elem , Brandt Sebastian , Calvanese Diego , Ryzhikov Vladislav , Xiao Guohui , Zakharyaschev Michael

Identyfikatory

DOI

10.2478/amcs-2019-0002

• Języki publikacji: EN

Abstrakty

EN

Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use it to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA), which provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.

Słowa kluczowe

EN

metric temporal logic; ontology based data access; SPARQL query; Ontop platform

PL

logika temporalna; baza danych ontologiczna; język zapytań SPARQL; platforma Ontop

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2019
• Tom: Vol. 29, no. 1
• Strony: 17--30
• Opis fizyczny: Bibliogr. 46 poz., rys., tab., wykr.

Twórcy

autor

Güzel Kalaycı Elem

• KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100 Bolzano, Italy

autor

Brandt Sebastian

• Siemens CT, Otto-Hahn-Ring 6, 81739 München, Germany

autor

Calvanese Diego

• KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100 Bolzano, Italy

autor

Ryzhikov Vladislav

• Department of Computer Science and Information Systems, Birkbeck, University of London, Malet St., London WC1E 7HX, UK

autor

Xiao Guohui

• KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100 Bolzano, Italy

autor

Zakharyaschev Michael

• Department of Computer Science and Information Systems, Birkbeck, University of London, Malet St., London WC1E 7HX, UK; National Research University Higher School of Economics, 3 Kochnovsky Proezd, 125319, Moscow, Russia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).