Ontology-based Access to Temporal Data with Ontop: A Framework Proposal

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

Applied Mathematics and Computer Science. 29(1):17--30 2019.

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 this knowledge 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.

  title = "Ontology-based Access to Temporal Data with Ontop:  A Framework
   year = "2019",
   author = "Güzel Kalayci, Elem and Sebastian Brandt and Diego
Calvanese and Vladislav Ryzhikov and Guohui Xiao and Michael
   journal = "Applied Mathematics and Computer Science",
   pages = "17--30",
   number = "1",
   volume = "29",
   doi = "10.2478/amcs-2019-0002",
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