Descriptive Types for Linked Data Resources

  • Gabriel Ciobanu
  • Ross HorneEmail author
  • Vladimiro Sassone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8974)


This work introduces the notion of descriptive typing. Type systems are typically prescriptive in the sense that they prescribe a space of permitted programs. In contrast, descriptive types assigned to resources in Linked Data provide useful annotations that describe how a resource may be used. Resources are represented by URIs that have no internal structure, hence there is no a priori type for a resource. Instead of raising compile time errors, a descriptive type system raises runtime warnings with a menu of options that make suggestions to the programmer. We introduce a subtype system, algorithmic type system and operational semantics that work together to characterise how descriptive types are used. The type system enables RDF Schema inference and several other modes of inference that are new to Linked Data.


Type System Resource Description Framework Link Data Operational Semantic Atomic Type 
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.



We are grateful to the organisers of the Ershov memorial conference, PSI 2014, for inviting this work to be presented as a keynote speech. The work of the first and second authors was supported by a grant of the Romanian National Authority for Scientific Research, project number PN-II-ID-PCE-2011-3-0919.


  1. 1.
    Barbanera, F., Dezani-Ciancaglini, M., de’Liguoro, U.: Intersection and union types: syntax and semantics. Inf. Comput. 119(2), 202–230 (1995)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Berners-Lee, T.: Linked data. Int. J. Semant. Web Inf. Syst. 4(2), 1 (2006)Google Scholar
  3. 3.
    Bizer, C., et al.: DBpedia: a crystallization point for the web of data. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)CrossRefGoogle Scholar
  4. 4.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)Google Scholar
  5. 5.
    Brickley, D., Guha, R.V.: RDF vocabulary description language 1.0: RDF schema. Edited Recommendation PER-rdf-schema-20140109, W3C (2014)Google Scholar
  6. 6.
    Ciobanu, G., Horne, R., Sassone, V.: Local type checking for linked data consumers. In: Ravara, A., Silva, J. (eds.) WWV. EPTCS, vol. 123, pp. 19–33 (2013)Google Scholar
  7. 7.
    Cyganiak, R., Wood, D., Lanthaler, M.: RDF 1.1 concepts and abstract syntax. Recommendation REC-rdf11-concepts-20140225, W3C (2014)Google Scholar
  8. 8.
    Dershowitz, N., Manna, Z.: Proving termination with multiset orderings. Commun. ACM 22(8), 465–476 (1979)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Dezani-Ciancaglini, M., Horne, R., Sassone, V.: Tracing where and who provenance in linked data: a calculus. Theor. Comput. Sci. 464, 113–129 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Harris, S., Seaborne, A.: SPARQL 1.1 query language. Recommendation REC-sparql11-query-20130321, W3C. MIT, MA (2013)Google Scholar
  11. 11.
    Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P.F., Rudolph, S.: OWL 2 Web Ontology Language primer (second edition). Recommendation REC-owl2-primer-20121211, W3C (2012)Google Scholar
  12. 12.
    Horne, R., Sassone, V.: A verified algebra for read-write linked data. Sci. Comput. Program. 89(A), 2–22 (2014)CrossRefGoogle Scholar
  13. 13.
    MacNeille, H.M.: Extensions of partially ordered sets. Proc. Natl. Acad. Sci. U.S.A. 22(1), 45–50 (1936)CrossRefzbMATHGoogle Scholar
  14. 14.
    Muñoz, S., Pérez, J., Gutierrez, C.: Simple and efficient minimal RDFS. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 220–234 (2009)CrossRefGoogle Scholar
  15. 15.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. (TODS) 34(3), 16 (2009)CrossRefGoogle Scholar
  16. 16.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of 16th WWW Conference, pp. 697–706. ACM (2007)Google Scholar
  17. 17.
    Tiuryn, J.: Subtype inequalities. In: Proceedings of the Seventh Annual IEEE Symposium on Logic in Computer Science, LICS 1992, pp. 308–315. IEEE (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Gabriel Ciobanu
    • 1
  • Ross Horne
    • 1
    • 2
    Email author
  • Vladimiro Sassone
    • 3
  1. 1.Institute of Computer ScienceRomanian AcademyIaşiRomania
  2. 2.Faculty of Information TechnologyKazakh-British Technical UniversityAlmatyKazakhstan
  3. 3.Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK

Personalised recommendations