Skip to main content
Book cover

OTM Confederated International Conferences "On the Move to Meaningful Internet Systems"

OTM 2016: On the Move to Meaningful Internet Systems: OTM 2016 Conferences pp 654–672Cite as

Validating Interlinks Between Linked Data Datasets with the SUMMR Methodology

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 10033)

Abstract

Linked Data datasets use interlinks to connect semantically similar resources across datasets. As datasets evolve, a resources locator can change which can cause interlinks that contain old resource locators, to no longer dereference and become invalid. Validating interlinks, through validating the resource locators within them, when a dataset has changed is important to ensure interlinks work as intended. In this paper we introduce the SPARQL Usage for Mapping Maintenance and Reuse (SUMMR) methodology. SUMMR is an approach for Mapping Maintenance and Reuse (MMR) that provides query templates which are based on standard SPARQL queries for MMR activities. This paper describes SUMMR and two experiments: a lab-based evaluation of SUMMR’s mapping maintenance query templates and a deployment of SUMMR in the DBpedia v.2015-10 release to detect invalid interlinks. The lab-based evaluation involved detecting interlinks that have become invalid, due to changes in resource locators and the repair of the invalid interlinks. The results show that the SUMMR templates and approach can be used to effectively detect and repair invalid interlinks. SUMMR’s query template for discovering invalid interlinks was applied to the DBpedia v.2015-10 release, which discovered 53,418 invalid interlinks in that release.

Keywords

  • Linked data
  • Interlinks
  • Mapping maintenance
  • Quality
  • DBpedia

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-48472-3_39
  • Chapter length: 19 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-48472-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

Notes

  1. 1.

    http://www.scss.tcd.ie/~meehanal/Experiment3/.

  2. 2.

    http://dbpedia.org/sparql.

  3. 3.

    https://github.com/dbpedia/dbpedia-links.

  4. 4.

    http://wiki.dbpedia.org/Downloads2015-04.

References

  1. Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Discovering and Maintaining Links on the Web of Data. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  2. Umbrich, J., Hausenblas, M., Hogan, A., Polleres, A., Decker, S.: Towards dataset dynamics: change frequency of linked open data sources. In: Proceedings of the Linked Data on the Web Workshop (LDOW2010), Raleigh, North Carolina, USA, 27 April 2010

    Google Scholar 

  3. Popitsch, N., Haslhofer, B.: DSNotify–A solution for event detection and link maintenance in dynamic datasets. Web Semant. Sci. Serv. Agents World Wide Web 9(3), 266–283 (2011)

    CrossRef  Google Scholar 

  4. Meehan, A., Brennan, R., O’Sullivan, D.: SPARQL based mapping management. In: 2015 IEEE International Conference on Semantic Computing (ICSC), pp. 456–459. IEEE, February 2015

    Google Scholar 

  5. Euzenat, J., Shvaiko, P.: Ontology matching, vol. 18. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  6. Dos Reis, J.C., Pruski, C., Reynaud-Delaître, C.: State-of-the-art on mapping maintenance and challenges towards a fully automatic approach. Expert Syst. Appl. 42(3), 1465–1478 (2015)

    CrossRef  Google Scholar 

  7. Falconer, S., Noy, N.: Interactive techniques to support ontology matching. In: Schema Matching and Mapping, pp. 29–51. Springer, Heidelberg (2011)

    Google Scholar 

  8. Thomas, H., Brennan, R., O’Sullivan, D.: Using the OM 2 R meta-data model for ontology mapping reuse for the ontology alignment challenge–a case study. In: Ontology Matching, p. 61 (2012)

    Google Scholar 

  9. Zhdanova, A.V., Shvaiko, P.: Community-driven ontology matching. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 34–49. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  10. Aumueller, D., Do, H., Massmann, S., Rahm, E.: Schema and ontology matching with COMA++. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 906–908. ACM (2005)

    Google Scholar 

  11. Bizer, C., Schultz, A.: The R2R framework: publishing and discovering mappings on the web. In: 1st International Workshop on Consuming Linked Data (COLD 2010), Shanghai, China, November 2010

    Google Scholar 

  12. Thomas, H., Brennan, R., O’Sullivan, D.: MooM–a prototype framework for management of ontology mappings. In: 2011 IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 548–555. IEEE (2011)

    Google Scholar 

  13. Nentwig, M., Soru, T., Ngonga Ngomo, A.-C., Rahm, E.: LinkLion: a link repository for the web of data. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8798, pp. 439–443. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11955-7_63

    Google Scholar 

  14. Khattak, A.M., Pervez, Z., Khan, W.A., Khan, A.M., Latif, K., Lee, S.Y.: Mapping evolution of dynamic web ontologies. Inf. Sci. 303, 101–119 (2015)

    CrossRef  MathSciNet  Google Scholar 

  15. Dos Reis, J.C., Pruski, C., Da Silveira, M., Reynaud-Delaître, C.: DyKOSMap: a framework for mapping adaptation between biomedical knowledge organization systems. J. Biomed. Inf. 55, 153–173 (2015)

    CrossRef  Google Scholar 

  16. Hartung, M., Groß, A., Rahm, E.: COnto–Diff: generation of complex evolution mappings for life science ontologies. J. Biomed. Inform. 46(1), 15–32 (2013)

    CrossRef  Google Scholar 

  17. Meehan, A., Brennan, R., Lewis, D., O’Sullivan, D.: Mapping representation based on meta-data and SPIN for localization workflows. In: Proceedings of the Second International Workshop on Semantic Web Enterprise Adoption and Best Practice at ESWC (2014)

    Google Scholar 

  18. Noy, N.F., Griffith, N., Musen, M.A.: Collecting community-based mappings in an ontology repository. In: Sheth, A., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 371–386. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88564-1_24

    CrossRef  Google Scholar 

  19. Dos Reis, J.C., Pruski, C., Da Silveira, M., Reynaud-Delaître, C.: Analyzing and supporting the mapping maintenance problem in biomedical knowledge organization systems. In: Proceedings of the Workshop on Semantic Interoperability in Medical Informatics collocated with the 9th Extended Semantic Web Conference, pp. 25–36 (2012)

    Google Scholar 

  20. Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)

    CrossRef  Google Scholar 

  21. Knublauch, H.: SPIN SPARQL Inferencing Notation. http://spinrdf.org/. Accessed 7 July 2016

Download references

Acknowledgements

This research is supported by Science Foundation Ireland through the CNGL Program (Grant 12/CE/I2267) in the ADAPT Centre (www.adaptcentre.ie) at Trinity College Dublin and funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 644055 (ALIGNED, http://www.aligned-project.eu).

Author information

Authors and Affiliations

  1. Knowledge and Data Engineering Group, School of Computer Science and Statistics, ADAPT Centre, Trinity College Dublin, Dublin, Ireland

    Alan Meehan, Rob Brennan & Declan O’Sullivan

  2. AKSW, University of Leipzig, Computer Science, Leipzig, Germany

    Dimitris Kontokostas & Markus Freudenberg

Authors
  1. Alan Meehan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitris Kontokostas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Markus Freudenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rob Brennan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Declan O’Sullivan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alan Meehan .

Editor information

Editors and Affiliations

  1. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Christophe Debruyne

  2. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  3. TU Graz, Graz, Austria

    Robert Meersman

  4. La Trobe University, Melbourne, Australia

    Tharam Dillon

  5. Institute of Computer Languages, TU Wien, Vienna, Austria

    eva Kühn

  6. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Declan O'Sullivan

  7. Università degli Studi di Milano Crema, Crema, Italy

    Claudio Agostino Ardagna

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Meehan, A., Kontokostas, D., Freudenberg, M., Brennan, R., O’Sullivan, D. (2016). Validating Interlinks Between Linked Data Datasets with the SUMMR Methodology. In: , et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48472-3_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48471-6

  • Online ISBN: 978-3-319-48472-3

  • eBook Packages: Computer ScienceComputer Science (R0)