<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 <italic>SPARQL</italic>-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.</p></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 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.

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

KRDB Research Centre for Knowledge and Data

Department of Computer Science and Information Systems, Birkbeck

National Research University Higher School of Economics

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Ontology\u2013based access to temporal data with Ontop: A framework proposal"}

Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge...

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

Online veröffentlicht: 29. März 2019

Seitenbereich: 17 - 30

Eingereicht: 05. Feb. 2018

Akzeptiert: 22. Sept. 2018

DOI: https://doi.org/10.2478/amcs-2019-0002

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

© 2019 Elem Güzel Kalayci et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 29. März 2019

Seitenbereich: 17 - 30

Eingereicht: 05. Feb. 2018

Akzeptiert: 22. Sept. 2018

DOI: https://doi.org/10.2478/amcs-2019-0002

Schlüsselwörtermetric temporal logic, ontology-based data access, query, Ontop

© 2019 Elem Güzel Kalayci et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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