A Datalog+ RuleML 1.01 Architecture for Rule-Based Data Access in Ecosystem Research

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8620)


Rule-Based Data Access (RBDA) enables automated reasoning over a knowledge base (KB) as a generalized global schema for the data in local (e.g., relational or graph) databases reachable through mappings. RBDA can semantically validate, enrich, and integrate heterogeneous data sources. This paper proposes an RBDA architecture layered on Datalog+ RuleML, and uses it for the ΔForest case study on the susceptibility of forests to climate change. Deliberation RuleML 1.01 was mostly motivated by Datalog customization requirements for RBDA. It includes Datalog+ RuleML 1.01 as a standard XML serialization of Datalog+, a superlanguage of the decidable Datalog±. Datalog+ RuleML is customized into the three Datalog extensions Datalog[∃], Datalog[=], and Datalog[\(\bot\)] through MYNG, the RuleML Modular sYNtax confiGurator generating (Relax NG and XSD) schemas from language-feature selections. The ΔForest case study on climate change employs data derived from three main forest monitoring networks in Switzerland. The KB includes background knowledge about the study sites and design, e.g., abundant tree species groups, pure tree stands, and statistical independence among forest plots. The KB is used to rewrite queries about, e.g., the eligible plots for studying a particular species group. The mapping rules unfold our newly designed global schema to the three given local schemas, e.g. for the grade of forest management. The RBDA/ΔForest case study has shown the usefulness of our approach to Ecosystem Research for global schema design and demonstrated how automated reasoning can become key to knowledge modeling and consolidation for complex statistical data analysis.


Global Schema Quercus Robur Conjunctive Query Ecosystem Research Stand Density Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Calvanese, D., et al.: Optique: OBDA solution for big data. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 293–295. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Baget, J.-F., Croitoru, M., da Silva, B.P.L.: ALASKA for ontology based data access. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 157–161. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Kühn, E., Puntigam, F., Elmagarmid, A.K.: Multidatabase transaction and query processing in logic. In: Elmagarmid, A.K. (ed.) Database Transaction Models for Advanced Applications. Morgan Kaufmann Publishers (1991)Google Scholar
  4. 4.
    Lakshmanan, L.V.S., Sadri, F., Subramanian, I.N.: On the logical foundations of schema integration and evolution in heterogeneous database systems. In: Ceri, S., Tsur, S., Tanaka, K. (eds.) DOOD 1993. LNCS, vol. 760, pp. 81–100. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  5. 5.
    Bassiliades, N., Vlahavas, L., Elmagarmid, A.K., Houstis, E.N.: InterBase-KB: Integrating a knowledge base system with a multidatabase system for data warehousing. IEEE Transactions on Knowledge and Data Engineering 15(5), 1188–1205 (2003)CrossRefGoogle Scholar
  6. 6.
    Calvanese, D., Giacomo, G.D., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., Rosati, R., Ruzzi, M., Savo, D.F.: The MASTRO system for ontology-based data access. Semantic Web Journal 2(1), 43–53 (2011)Google Scholar
  7. 7.
    De Virgilio, R., Orsi, G., Tanca, L., Torlone, R.: NYAYA: A system supporting the uniform management of large sets of semantic data. In: IEEE 28th International Conference on Data Engineering, pp. 1309–1312 ( April 2012)Google Scholar
  8. 8.
    Motik, B., Nenov, Y., Piro, R., Horrocks, I.: Parallel materialisation of Datalog programs in centralised, main-memory RDF systems. To appear in AAAI (2014)Google Scholar
  9. 9.
    Rigling, A., Zingg, A.: Relative Mortalität als Indikator für die Sensitivität von Waldbeständen. WSL Projekt, Bew-Pin 201104N0134Google Scholar
  10. 10.
    Calì, A., Gottlob, G., Lukasiewicz, T.: A general Datalog-based framework for tractable query answering over ontologies. Journal of Web Semantics 14, 57–83 (2012)CrossRefGoogle 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. Journal of Automated Reasoning 39(3), 385–429 (2007)zbMATHMathSciNetCrossRefGoogle Scholar
  12. 12.
    Motik, B., Cuenca Grau, B., Horrocks, I., Wu, Z., Fokoue, A., Lutz, C.: OWL 2 Web Ontology Language Profiles, W3C Recommendation, 2nd edn. (October 2009),
  13. 13.
    Grosof, B.N., Horrocks, I., Volz, R., Decker, S.: Description logic programs: Combining logic programs with description logic. In: Proceedings of the 12th International Conference on World Wide Web, WWW 2003, pp. 48–57 (2003)Google Scholar
  14. 14.
    Boley, H., Hallmark, G., Kifer, M., Paschke, A., Polleres, A., Reynolds, D.: RIF Core Dialect, W3C Recommendation, 2nd edn. (February 2013),
  15. 15.
    Eiter, T., Gottlob, G., Mannila, H.: Disjunctive Datalog. ACM Trans. Database Syst. 22(3), 364–418 (1997)CrossRefGoogle Scholar
  16. 16.
    Gottlob, G., Orsi, G., Pieris, A.: Ontological queries: Rewriting and optimization. In: Abiteboul, S., Böhm, K., Koch, C., Tan, K.L. (eds.) Proceedings of the 27th International Conference on Data Engineering, ICDE 2011, pp. 2–13. IEEE Computer Society, Hannover (2011)CrossRefGoogle Scholar
  17. 17.
    Calvanese, D., De Giacomo, G., Lenzerini, M., Vardi, M.Y.: Query processing under GLAV mappings for relational and graph databases. Proc. of the VLDB Endowment 6(2), 61–72 (2012)CrossRefGoogle Scholar
  18. 18.
    Cuenca Grau, B., Motik, B., Stoilos, G., Horrocks, I.: Computing Datalog rewritings beyond Horn ontologies. In: Proc. of the 23rd Int. Joint Conf. on Artificial Intelligence, IJCAI 2013 (2013)Google Scholar
  19. 19.
    Athan, T., Boley, H.: Design and implementation of highly modular schemas for XML: Customization of RuleML. In: Palmirani, M., Sottara, D., Olken, F. (eds.) RuleML - America 2011. LNCS, vol. 7018, pp. 17–32. Springer, Heidelberg (2011)Google Scholar
  20. 20.
    Rodríguez-Muro, M., Kontchakov, R., Zakharyaschev, M.: Ontology-based data access: Ontop of databases. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 558–573. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Boley, H.: A RIF-Style Semantics for RuleML-Integrated Positional-Slotted, Object-Applicative Rules. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 194–211. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  22. 22.
    Pretzsch, H., Biber, P.: A Re-evaluation of Reineke’s rule and stand density index. For. Sci. 51, 304–320 (2005)Google Scholar
  23. 23.
    Reineke, L.: Perfecting a stand density index for even-aged forests. J. Agric. Res. 46, 627–638 (1933)Google Scholar
  24. 24.
    Schütz, J.P., Zingg, A.: Improving estimations of maximal stand density by combining Reineke’s size-density rule and yield level, using the example of spruce (Picea abies (L.) Karst.) and European Beech (Fagus sylvatica L.). Ann. For. Sci. 67 (2010)Google Scholar
  25. 25.
    Zingg, A., Bachofen, H.: Wachstumsforschung an der WSL. Schweizer Wald 134(9), 15–23 (1998)Google Scholar
  26. 26.
    Brang, P., Commarmot, B., Rohrer, L., Bugmann, H.: Monitoringkonzept für Naturwaldreservate in der Schweiz. Eidg. Forschungsanstalt für Wald, Schnee und Landschaft WSL; ETH Zürich, Professur für Waldökologie, Birmensdorf, Zürich (February 2008),
  27. 27.
    Dobbertin, M., Kindermann, G., Neumann, M.: Analysis of forest growth data on intensive monitoring plots. In: Fischer, R., Lortenz, M. (eds.) Forest Condition in Europe: Technical Report of ICP Forests and FutMon, pp. 115–127. Institute for World Forestry, Hamburg (2011)Google Scholar
  28. 28.
    Bak, J., Brzykcy, G.z., Jedrzejek, C.: Extended rules in knowledge-based data access. In: Palmirani, M., Sottara, D., Olken, F. (eds.) RuleML - America 2011. LNCS, vol. 7018, pp. 112–127. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversity of New BrunswickFrederictonCanada
  2. 2.Swiss Federal Research Institute WSLBirmensdorfSwitzerland
  3. 3.Athan Services ( LafayetteUSA

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