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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alur, R., Henzinger, T.A.: Real-time logics: complexity and expressiveness. Inf. Comput. 104(1), 35–77 (1993)
Artale, A., Calvanese, D., Kontchakov, R., Zakharyaschev, M.: The DL-Lite family and relations. J. Artif. Intell. Res. 36(1), 1–69 (2009)
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)
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)
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
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)
Borgwardt, S., Lippmann, M., Thost, V.: Temporal query answering in the description logic DL-Lite. In: Proceedings of FroCoS 2013, pp. 165–180 (2013)
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)
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)
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 Journal
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)
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)
R. S. P. C. Group. RDF stream processing: Requirements and design principles. W3C draft community group report, W3C (2016)
Gutiérrez-Basulto, V., Jung, J.C., Ozaki, A.: On metric temporal description logics. In: ECAI 2016, pp. 837–845 (2016)
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
Harris, S., Seaborne, A., Prud’hommeaux, E.: SPARQL 1.1 query language. W3C recommendation, W3C (2013)
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)
Klarman, S., Meyer, T.: Querying temporal databases via OWL 2 QL. In: Proceedings of RR 2014, pp. 92–107 (2014)
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
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)
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)
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
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
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
W3C. SPARQL 1.1 entailment regimes. Technical report, W3C, March 2013
W3C. Time ontology in OWL. W3C working draft, OGC & W3C (2017)
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
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Brandt, S., Kalaycı, E.G., Ryzhikov, V., Xiao, G., Zakharyaschev, M. (2017). A Framework for Temporal Ontology-Based Data Access: A Proposal. In: Kirikova, M., et al. New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-319-67162-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-67162-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67161-1
Online ISBN: 978-3-319-67162-8
eBook Packages: Computer ScienceComputer Science (R0)