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A Framework for Temporal Ontology-Based Data Access: A Proposal

  • Sebastian Brandt
  • Elem Güzel Kalaycı
  • Vladislav Ryzhikov
  • Guohui XiaoEmail author
  • Michael Zakharyaschev
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 767)

Abstract

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) that 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 of temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in 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.

Keywords

Temporal logic Ontology-based data access Ontop 

Notes

Acknowledgements

This research has been partially supported by the project “Ontology-based analysis of temporal and streaming data” (OBATS), funded through the 2017 call issued by the Research Committee of the Free University of Bozen-Bolzano.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sebastian Brandt
    • 1
  • Elem Güzel Kalaycı
    • 2
  • Vladislav Ryzhikov
    • 2
  • Guohui Xiao
    • 2
    Email author
  • Michael Zakharyaschev
    • 3
  1. 1.Siemens CTMunichGermany
  2. 2.KRDB Research Centre for Knowledge and Data, Free University of Bozen-BolzanoBolzanoItaly
  3. 3.Department of Computer Science and Information SystemsBirkbeck, University of LondonLondonUK

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