International Journal of applied mathematics and computer science

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Paper details

Number 1 - March 2019
Volume 29 - 2019

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

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

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.

metric temporal logic, ontology-based data access, SPARQL query, Ontop