StdTrip: Promoting the Reuse of Standard Vocabularies in Open Government Data

  • Percy Salas
  • José Viterbo
  • Karin Breitman
  • Marco Antonio Casanova


Linked Data is the standard generally adopted for publishing Open Government Data. This operation requires that a myriad of public information datasets be converted to a set of RDF triples. A major step in this process is deciding how to represent the database schema concepts in terms of RDF classes and properties. This is done by mapping database concepts to a vocabulary, which will be used as the base for generating the RDF representation. The construction of this vocabulary is extremely important, because it determines how the generated triples interlink the resulting dataset with other existing ones. However, most engines today provide support only to the mechanical process of transforming relational to RDF data. In this chapter, we discuss this process and present the StdTrip framework, a tool that supports the conceptual modeling stages of the production of RDF datasets, promoting the reuse of W3C recommended standard RDF vocabularies or suggesting the reuse of non-standard vocabularies already adopted by other RDF datasets.


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  1. 1.
    Improving access to government through better use of the web (2009). URL
  2. 2.
    Publishing open government data (2009). URL
  3. 3.
    Allemang, D., Hendler, J.: Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. Morgan Kaufmann (2008) 132Google Scholar
  4. 4.
    Auer, S., Dietzold, S., Lehmann, J., Hellmann, S., Aumueller, D.: Triplify: light-weight linked data publication from relational databases. In: WWW ’09: Proceedings of the 18th international conference on World wide web, pp. 621–630. ACM, New York, NY, USA (2009). DOI
  5. 5.
    Barrasa, J., Corcho, O., Gómez-Pérez, A.: R2O, an extensible and semantically based database-to-ontology mapping language, vol. 3372 (2004)Google Scholar
  6. 6.
    Batini, C., Ceri, S., Navathe, S.B.: Conceptual database design: an Entity-relationship approach (1991)Google Scholar
  7. 7.
    Berners-Lee, T.: Cool uris don’t change. Retrieved January 10, 2010, from (1998)
  8. 8.
    Berrueta, D., Phipps, J.: Best practice recipes for publishing rdf vocabularies – w3c working group note. Retrieved December 14, 2010, from (2008)
  9. 9.
    Bizer, C., Cyganiak, R., Heath, T.: How to publish linked data on the web. Retrieved December 14, 2010, from (2007)
  10. 10.
    Bizer, C., Heath, T., Ayers, D., Raimond, Y.: Interlinking Open Data on the Web (Poster). In: In Demonstrations Track, 4th European Semantic Web Conference (ESWC2007) (2007)Google Scholar
  11. 11.
    Bizer, C., Seaborne, A.: D2RQ-treating non-RDF databases as virtual RDF graphs (2004)Google Scholar
  12. 12.
    Breitman, K., Casanova, M.A., Truszkowski, W.: Semantic Web: Concepts, Technologies and Applications (NASA Monographs in Systems and Software Engineering). Springer-Verlag New York, Inc., Secaucus, NJ, USA (2006)Google Scholar
  13. 13.
    Breslin, J., Passant, A., Decker, S.: The Social Semantic Web. Springer Publishing Company, Incorporated (2009)CrossRefGoogle Scholar
  14. 14.
    Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations, pp. 74–83 (2004)Google Scholar
  15. 15.
    Casanova, M.A., Breitman, K., Brauner, D., Marins, A.: Database conceptual schema matching. IEEE Computer 40(10), 102–104 (2007)Google Scholar
  16. 16.
    Casanova, M.A., Lauschner, T., Leme, L.A.P., Breitman, K., Furtado, A.L., Vidal, V.: A strategy to revise the constraints of the mediated schema. In: Proc. of the 28th Int’l. Conf. on Conceptual Modeling, Lecture Notes in Computer Science, vol. 5829, pp. 265–279. Springer (2009). DOI 10.1007\/978-3-642-04840-1\_21Google Scholar
  17. 18.
    Casanova, M.A., de Sá, J.E.A.: Mapping uninterpreted schemes into entity-relationship diagrams: two applications to conceptual schema design. IBM Journal of Research and Development 28, 82–94 (1984). DOI
  18. 19.
    Cerbah, F.: Learning highly structured semantic repositories from relational databases. The Semantic Web: Research and Applications pp. 777–781 (2008)Google Scholar
  19. 20.
    Codd, E.F.: A relational model of data for large shared data banks. Communications of the ACM 13, 377–387 (1970). ACM ID: 362685Google Scholar
  20. 21.
    Cullot, N., Ghawi, R., Yétongnon, K.: DB2OWL: A Tool for Automatic Database-to-Ontology Mapping, pp. 491–494 (2007)Google Scholar
  21. 22.
    d‘Aquin, M., Sabou, M., Dzbor, M., Baldassarre, C., Gridinoc, L., Angeletou, S., Motta, E.: Watson: A gateway for the semantic web (2007)Google Scholar
  22. 23.
    David, J.: AROMA results for OAEI 2009 (2009)Google Scholar
  23. 24.
    Do, H.H.: Schema matching and mapping-based data integration (2006). URL
  24. 25.
    Do, H.H., Rahm, E.: COMA: a system for flexible combination of schema matching approaches, pp. 610–621. VLDB ’02. VLDB Endowment (2002). URL ACM ID: 1287422
  25. 26.
    Du, H., Wery, L.: Micro: A normalization tool for relational database designers. Journal of Network and Computer Applications 22(4), 215–232 (1999). DOI  10.1006/jnca.1999.0096 CrossRefGoogle Scholar
  26. 27.
    Erling, O., Mikhailov, I.: Rdf support in the virtuoso dbms. Networked Knowledge-Networked Media pp. 7–24 (2009) Percy Salas, José Viterbo, Karin Breitman, and Marco Antonio Casanova 6 StdTrip: Promoting the Reuse of Standard Vocabularies in OGD 133Google Scholar
  27. 28.
    Euzenat, J., Ferrara, A., Hollink, L., et al.: Results of the ontology alignment evaluation initiative 2009. In: Proc. 4th of ISWC Workshop on Ontology Matching (OM) (2009)Google Scholar
  28. 29.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer-Verlag, Heidelberg (DE) (2007)zbMATHGoogle Scholar
  29. 30.
    Fahad, M.: Er2owl: Generating owl ontology from er diagram. Intelligent Information Processing IV pp. 28–37 (2008)Google Scholar
  30. 31.
    Heath, T., Bizer, C.: Linked Data. Morgan & Claypool Publishers (2011)Google Scholar
  31. 32.
    Heuser, C.A.: Projeto de banco de dados. Sagra Luzzatto (2004)Google Scholar
  32. 33.
    Kinsella, S., Bojars, U., Harth, A., Breslin, J.G., Decker, S.: An interactive map of semantic web ontology usage. In: IV ’08: Proceedings of the 2008 12th International Conference Information Visualisation, pp. 179–184. IEEE Computer Society, Washington, DC, USA (2008). DOI 10.1109/IV.2008.60
  33. 34.
    Leme, L.A.P., Casanova, M.A., Breitman, K., Furtado, A.L.: Owl schema matching. Journal of the Brazilian Computer Society 16(1), 21–34 (2010). DOI  10.1007/s13173-010-0005 3MathSciNetCrossRefGoogle Scholar
  34. 35.
    Myroshnichenko, I., Murphy, M.C.: Mapping ER Schemas to OWL Ontologies, vol. 0, pp. 324–329. IEEE Computer Society (2009). DOI
  35. 36.
    Piccinini, H., Lemos, M., Casanova, M.A., Furtado, A.: W-Ray: A Strategy to Publish Deep Web Geographic Data. In: Proceedings of the 4th International Workshop on Semantic and Conceptual Issues in GIS (SeCoGIS 2010), to appear (2010)Google Scholar
  36. 37.
    Polfliet, S., Ichise, R.: Automated mapping generation for converting databases into linked data. Proc. of ISWC2010Google Scholar
  37. 38.
    Prud’hommeaux, E., Hausenblas, M.: Use cases and requirements for mapping relational databases to rdf. Retrieved December 18, 2010, from (2010)
  38. 39.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. The VLDB Journal 10(4), 334–350 (2001). DOI
  39. 40.
    S., D., S., S., R., C.: R2rml: Rdb to rdf mapping language. w3c rdb2rdf working group. Retrieved December 15, 2010, from (2010)
  40. 41.
    Sahoo, S.S., Halb, W., Hellmann, S., Idehen, K., Thibodeau Jr, T., Auer, S., Sequeda, J., Ezzat, A.: A survey of current approaches for mapping of relational databases to rdf. W3C RDB2RDF Incubator Group report (2009)Google Scholar
  41. 44.
    Sauermann, L., Cyganiak, R.: Cool uris for the semantic web. Retrieved January 18, 2010, from (2008)
  42. 45.
    Seddiqui, M.H., Aono, M.: Anchor-Flood: Results for OAEI-2009Google Scholar
  43. 46.
    Sequeda, J.F., Depena, R., Miranker, D.P.: Ultrawrap: Using sql views for rdb2rdf. Proc. of ISWC2009Google Scholar
  44. 47.
    Sorrentino, S., Bergamaschi, S., Gawinecki, M., Po, L.: Schema normalization for improving schema matching. In: Proc. of the 28th International Conference on Conceptual Modeling(ER ’09), pp. 280–293. Springer-Verlag, Berlin, Heidelberg (2009). DOI\_22Google Scholar
  45. 48.
    Tirmizi, S., Sequeda, J., Miranker, D.: Translating sql applications to the semantic web, pp. 450–464 (2008)Google Scholar
  46. 49.
    Wang, J., Wen, J.R., Lochovsky, F., Ma, W.Y.: Instance-based schema matching for web databases by domain-specific query probing. In: Proc. of the 13th international conference on Very large data bases (VLDB ’04), pp. 408–419. VLDB Endowment (2004)Google Scholar
  47. 50.
    Wang, P., Xu, B.: Lily: Ontology alignment results for OAEI 2009 (2009)Google Scholar
  48. 51.
    Wang, S.L., Shen, J.W., Hong, T.P.: Mining fuzzy functional dependencies from quantitative data, vol. 5, pp. 3600–3605 vol.5 (2000). DOI  10.1109/ICSMC.2000.886568

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Percy Salas
    • 1
  • José Viterbo
    • 2
  • Karin Breitman
    • 1
  • Marco Antonio Casanova
    • 1
  1. 1.Departamento de InformáticaPontifícia Universidade Católica do Rio de JaneiroRio de JaneiroBrazil
  2. 2.Instituto de ComputaçãoUniversidade Federal FluminenseNiteróiBrazil

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