Advertisement

Conceptual Schema Transformation in Ontology-Based Data Access

  • Diego CalvaneseEmail author
  • Tahir Emre Kalayci
  • Marco Montali
  • Ario Santoso
  • Wil van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11313)

Abstract

Ontology-based Data Access (OBDA) is a by now well-established paradigm that relies on conceptually representing a domain of interest to provide access to relational data sources. The conceptual representation is given in terms of a domain schema (also called an ontology), which is linked to the data sources by means of declarative mapping specifications, and queries posed over the conceptual schema are automatically rewritten into queries over the sources. We consider the interesting setting where users would like to access the same data sources through a new conceptual schema, which we call the upper schema. This is particularly relevant when the upper schema is a reference model for the domain, or captures the data format used by data analysis tools. We propose a solution to this problem that is based on using transformation rules to map the upper schema to the domain schema, building upon the knowledge contained therein. We show how this enriched framework can be automatically transformed into a standard OBDA specification, which directly links the original relational data sources to the upper schema. This allows us to access data directly from the data sources while leveraging the domain schema and upper schema as a lens. We have realized the framework in a tool-chain that provides modeling of the conceptual schemas, a concrete annotation-based mechanism to specify transformation rules, and the automated generation of the final OBDA specification.

Keywords

Conceptual schema transformation Ontology-based data access Ontology-to-ontology mapping 

Notes

Acknowledgements

This research is supported by the Euregio IPN12 KAOS (Knowledge-Aware Operational Support) project, funded by the “European Region Tyrol-South Tyrol-Trentino” (EGTC), and by the UNIBZ internal project OnProm (ONtology-driven PROcess Mining).

References

  1. 1.
    van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28108-2_19CrossRefGoogle Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation and Applications. CUP, New York (2003)zbMATHGoogle Scholar
  3. 3.
    Calvanese, D., et al.: Ontop: answering SPARQL queries over relational databases. Semant. Web J. 8(3), 471–487 (2017)CrossRefGoogle Scholar
  4. 4.
    Calvanese, D., et al.: Ontologies and databases: the DL-Lite approach. In: Tessaris, S. (ed.) Reasoning Web 2009. LNCS, vol. 5689, pp. 255–356. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-03754-2_7CrossRefGoogle Scholar
  5. 5.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. JAR 39(3), 385–429 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Calvanese, D., Kalayci, T.E., Montali, M., Santoso, A.: OBDA for log extraction in process mining. In: Ianni, G., et al. (eds.) Reasoning Web 2017. LNCS, vol. 10370, pp. 292–345. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61033-7_9CrossRefGoogle Scholar
  7. 7.
    Calvanese, D., Kalayci, T.E., Montali, M., Santoso, A.: The onprom toolchain for extracting business process logs using ontology-based data access. In: Proceedings of the BPM Demo Track and BPM Dissertation Award, Co-located with BPM 2017, vol. 1920. CEUR (2017)Google Scholar
  8. 8.
    Calvanese, D., Kalayci, T.E., Montali, M., Tinella, S.: Ontology-based data access for extracting event logs from legacy data: the onprom tool and methodology. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 288, pp. 220–236. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59336-4_16CrossRefGoogle Scholar
  9. 9.
    Catarci, T., Lenzerini, M.: Representing and using interschema knowledge in cooperative information systems. JICIS 2(4), 375–398 (1993)Google Scholar
  10. 10.
    Chopra, A.K., Singh, M.P.: Custard: computing norm states over information stores. In: Proceedings of AAMAS, pp. 1096–1105 (2016)Google Scholar
  11. 11.
    Daraio, C., et al.: The advantages of an ontology-based data management approach: openness, interoperability and data quality. Scientometrics 108(1), 441–455 (2016)CrossRefGoogle Scholar
  12. 12.
    Euzenat, J., Shvaiko, P.: Ontology Matching, 2nd edn. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38721-0CrossRefzbMATHGoogle Scholar
  13. 13.
    Guizzardi, G.: On ontology, ontologies, conceptualizations, modeling languages, and (meta)models. In: Proceedings of DB&IS, pp. 18–39. IOS Press (2006)Google Scholar
  14. 14.
    IEEE Computational Intelligence Society: IEEE standard for eXtensible Event Stream (XES) for achieving interoperability in event logs and event streams. Std 1849–2016. IEEE (2016)Google Scholar
  15. 15.
    Kharlamov, E., et al.: Ontology based data access in Statoil. J. Web Semant. 44, 3–36 (2017)CrossRefGoogle Scholar
  16. 16.
    Lenzerini, M.: Data integration: a theoretical perspective. In: Proceedings of PODS (2002)Google Scholar
  17. 17.
    Mehdi, G., et al.: Semantic rule-based equipment diagnostics. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 314–333. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68204-4_29CrossRefGoogle Scholar
  18. 18.
    Montali, M., Calvanese, D., De Giacomo, G.: Verification of data-aware commitment-based multiagent systems. In: Proceedings of AAMAS, pp. 157–164 (2014)Google Scholar
  19. 19.
    Motik, B., Cuenca Grau, B., Horrocks, I., Wu, Z., Fokoue, A., Lutz, C.: OWL 2 Web Ontology Language Profiles, 2nd edn. W3C Recommendation, W3C (2012)Google Scholar
  20. 20.
    Nardi, J.C., et al.: A commitment-based reference ontology for services. Inf. Syst. 54, 263–288 (2015)CrossRefGoogle Scholar
  21. 21.
    Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. J. Data Semant. X, 133–173 (2008)Google Scholar
  22. 22.
    Scherp, A., Saathoff, C., Franz, T., Staab, S.: Designing core ontologies. Appl. Ontol. 6(3), 177–221 (2011)Google Scholar
  23. 23.
    Xiao, G., et al.: Ontology-based data access: a survey. In: Proceedings of IJCAI. AAAI Press (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Diego Calvanese
    • 1
    Email author
  • Tahir Emre Kalayci
    • 1
    • 2
  • Marco Montali
    • 1
  • Ario Santoso
    • 1
    • 3
  • Wil van der Aalst
    • 4
  1. 1.KRDB Research Centre for Knowledge and DataFree University of Bozen-BolzanoBolzanoItaly
  2. 2.Virtual Vehicle Research CenterGrazAustria
  3. 3.Department of Computer ScienceUniversity of InnsbruckInnsbruckAustria
  4. 4.Process and Data Science (PADS)RWTH Aachen UniversityAachenGermany

Personalised recommendations