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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
First Online:
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 
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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.

References

  1. 1.
    Alur, R., Henzinger, T.A.: Real-time logics: complexity and expressiveness. Inf. Comput. 104(1), 35–77 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Artale, A., Calvanese, D., Kontchakov, R., Zakharyaschev, M.: The DL-Lite family and relations. J. Artif. Intell. Res. 36(1), 1–69 (2009)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Artale, A., Kontchakov, R., Kovtunova, A., Ryzhikov, V., Wolter, F., Zakharyaschev, M.: First-order rewritability of temporal ontology-mediated queries. In: Proceedings of IJCAI 2015, pp. 2706–2712. AAAI Press (2015)Google Scholar
  4. 4.
    Artale, A., Kontchakov, R., Wolter, F., Zakharyaschev, M.: Temporal description logic for ontology-based data access. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013. IJCAI/AAAI (2013)Google Scholar
  5. 5.
    Baader, F., Borgwardt, S., Lippmann, M.: Temporalizing ontology-based data access. In: Bonacina, M.P. (ed.) CADE 2013. LNCS, vol. 7898, pp. 330–344. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38574-2_23 CrossRefGoogle Scholar
  6. 6.
    Basulto, V.G., Jung, J., Kontchakov, R.: Temporalized EL ontologies for accessing temporal data: complexity of atomic queries. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016). AAAI Press (2016)Google Scholar
  7. 7.
    Borgwardt, S., Lippmann, M., Thost, V.: Temporal query answering in the description logic DL-Lite. In: Proceedings of FroCoS 2013, pp. 165–180 (2013)Google Scholar
  8. 8.
    Brandt, S., Kalaycı, E.G., Kontchakov, R., Ryzhikov, V., Xiao, G., Zakharyaschev, M.: Ontology-based data access with a horn fragment of metric temporal logic. In: AAAI (2017)Google Scholar
  9. 9.
    Calvanese, D., Cogrel, B., Komla-Ebri, S., Kontchakov, R., Lanti, D., Rezk, M., Rodriguez-Muro, M., Xiao, G.: Ontop: answering SPARQL queries over relational databases. Semant. Web 8(3), 471–487 (2017)CrossRefGoogle Scholar
  10. 10.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., Rosati, R., Ruzzi, M., Savo, D.F.: The mastro system for ontology-based data access. Semant. Web J. 2(1), 43–53 (2011). Listed among the 5 most cited papers in the first five years of the Semantic Web JournalGoogle Scholar
  11. 11.
    Calvanese, D., Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. J. Autom. Reasoning 39(3), 385–429 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Giese, M., Soylu, A., Vega-Gorgojo, G., Waaler, A., Haase, P., Jiménez-Ruiz, E., Lanti, D., Rezk, M., Xiao, G., Özçep, Ö.L., Rosati, R.: Optique - zooming in on big data access. IEEE Comput. 48(3), 60–67 (2015)CrossRefGoogle Scholar
  13. 13.
    R. S. P. C. Group. RDF stream processing: Requirements and design principles. W3C draft community group report, W3C (2016)Google Scholar
  14. 14.
    Gutiérrez-Basulto, V., Jung, J.C., Ozaki, A.: On metric temporal description logics. In: ECAI 2016, pp. 837–845 (2016)Google Scholar
  15. 15.
    Gutiérrez-Basulto, V., Klarman, S.: Towards a unifying approach to representing and querying temporal data in description logics. In: Krötzsch, M., Straccia, U. (eds.) RR 2012. LNCS, vol. 7497, pp. 90–105. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33203-6_8 CrossRefGoogle Scholar
  16. 16.
    Harris, S., Seaborne, A., Prud’hommeaux, E.: SPARQL 1.1 query language. W3C recommendation, W3C (2013)Google Scholar
  17. 17.
    Kharlamov, E., Brandt, S., Jimenez-Ruiz, E., Kotidis, Y., Lamparter, S., Mailis, T., Neuenstadt, C., Özçep, O., Pinkel, C., Svingos, C., Zheleznyakov, D., Horrocks, I., Ioannidis, Y., Moeller, R.: Ontology-based integration of streaming and static relational data with optique. In: Proceedings of the 2016 International Conference on Management of Data, SIGMOD 2016, pp. 2109–2112. ACM, New York (2016)Google Scholar
  18. 18.
    Klarman, S., Meyer, T.: Querying temporal databases via OWL 2 QL. In: Proceedings of RR 2014, pp. 92–107 (2014)Google Scholar
  19. 19.
    Kontchakov, R., Rezk, M., Rodríguez-Muro, M., Xiao, G., Zakharyaschev, M.: Answering SPARQL queries over databases under OWL 2 QL entailment regime. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 552–567. Springer, Cham (2014). doi: 10.1007/978-3-319-11964-9_35 Google Scholar
  20. 20.
    Möller, R., Özçep, Ö., Neuenstadt, C., Zheleznyakov, C., Kharlamov, E.: D5.1: a semantics for temporal and stream-based query answering in an obda context. Optique project deliverable, FP7-318338, EU (2013)Google Scholar
  21. 21.
    Motik, B., Fokoue, A., Horrocks, I., Wu, Z., Lutz, C., Cuenca Grau, B.: OWL Web Ontology Language profiles. W3C Recommendation, World Wide Web Consortium (2009)Google Scholar
  22. 22.
    Özçep, Ö.L., Möller, R., Neuenstadt, C.: A stream-temporal query language for ontology based data access. In: Lutz, C., Thielscher, M. (eds.) KI 2014. LNCS, vol. 8736, pp. 183–194. Springer, Cham (2014). doi: 10.1007/978-3-319-11206-0_18 Google Scholar
  23. 23.
    Poggi, A., Lembo, D., Calvanese, D., Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. In: Spaccapietra, S. (ed.) Journal on Data Semantics X. LNCS, vol. 4900, pp. 133–173. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-77688-8_5 CrossRefGoogle Scholar
  24. 24.
    Tappolet, J., Bernstein, A.: Applied temporal RDF: efficient temporal querying of RDF data with SPARQL. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 308–322. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02121-3_25 CrossRefGoogle Scholar
  25. 25.
    W3C. SPARQL 1.1 entailment regimes. Technical report, W3C, March 2013Google Scholar
  26. 26.
    W3C. Time ontology in OWL. W3C working draft, OGC & W3C (2017)Google Scholar
  27. 27.
    Xiao, G., Rezk, M., Rodríguez-Muro, M., Calvanese, D.: Rules and ontology based data access. In: Kontchakov, R., Mugnier, M.-L. (eds.) RR 2014. LNCS, vol. 8741, pp. 157–172. Springer, Cham (2014). doi: 10.1007/978-3-319-11113-1_11 Google Scholar

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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