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Semantic Web Datatype Similarity: Towards Better RDF Document Matching

  • Irvin Dongo
  • Firas Al Khalil
  • Richard Chbeir
  • Yudith Cardinale
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10438)

Abstract

With the advance of the Semantic Web, the need to integrate and combine data from different sources has increased considerably. Many efforts have focused on RDF document matching. However, they present limited approaches in the context of datatype similarity. This paper addresses the issue of datatype similarity for the Semantic Web as a first step towards a better RDF document matching. We propose a datatype hierarchy, based on W3C’s XSD datatype hierarchy, that better captures the subsumption relationship among primitive and derived datatypes. We also propose a new datatype similarity measure, that takes into consideration several aspects related to the new hierarchical relations between compared datatypes. Our experiments show that the new similarity measure, along with the new hierarchy, produces better results (closer to what a human expert would think about the similarity of compared datatypes) than the ones described in the literature.

Keywords

Datatype hierarchy Datatype similarity XML XML Schema Ontology RDF Semantic Web 

Notes

Acknowledgments

This work has been partly supported by FINCyT/ INOVATE PERU - Convenio No. 104-FINCyT-BDE-2014.

References

  1. 1.
    RDF 1.1 Semantics, W3C Recommendation 25 February 2014. https://www.w3.org/TR/rdf11-mt/#literals-and-datatypes
  2. 2.
    XML Schema Datatypes in RDF and OWL, W3C Working Group Note 14 March 2006. https://www.w3.org/TR/swbp-xsch-datatypes/#sec-values
  3. 3.
    Al-Bakri, M., Fairbairn, D.: Assessing similarity matching for possible integration of feature classifications of geospatial data from official and informal sources. Int. J. Geogr. Inf. Sci. 26(8), 1437–1456 (2012)CrossRefGoogle Scholar
  4. 4.
    Algergawy, A., Nayak, R., Saake, G.: XML schema element similarity measures: a schema matching context. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2009. LNCS, vol. 5871, pp. 1246–1253. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-05151-7_36 CrossRefGoogle Scholar
  5. 5.
    Algergawy, A., Nayak, R., Saake, G.: Element similarity measures in xml schema matching. Inf. Sci. 180(24), 4975–4998 (2010)CrossRefGoogle Scholar
  6. 6.
    Algergawy, A., Schallehn, E., Saake, G.: A sequence-based ontology matching approach. In: Proceedings of European Conference on Artificial Intelligence, Workshop on Contexts and Ontologies, pp. 26–30 (2008)Google Scholar
  7. 7.
    Algergawy, A., Schallehn, E., Saake, G.: Improving XML schema matching performance using prufer sequences. Data Knowl. Eng. 68(8), 728–747 (2009)CrossRefGoogle Scholar
  8. 8.
    Amarintrarak, N., Runapongsa, S., Tongsima, S., Wiwatwattana, N.: SAXM: semi-automatic xml schema mapping. In: Proceedings of International Technical Conference on Circuits/Systems, Computers and Communications, pp. 374–377 (2009)Google Scholar
  9. 9.
    Bernstein, P.A., Madhavan, J., Rahm, E.: Generic schema matching with cupid. Technical report MSR-TR-2001-58, pp. 1–14. Microsoft Research(2001)Google Scholar
  10. 10.
    Cruz, I.F., Antonelli, F.P., Stroe, C.: Agreementmaker: efficient matching for large real-world schemas and ontologies. Proc. VLDB 2(2), 1586–1589 (2009)CrossRefGoogle Scholar
  11. 11.
    Do, H.-H., Rahm, E.: Coma: a system for flexible combination of schema matching approaches. In: Proceedings of VLDB, pp. 610–621 (2002)Google Scholar
  12. 12.
    Eidoon, Z., Yazdani, N., Oroumchian, F.: Ontology matching using vector space. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 472–481. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-78646-7_45 CrossRefGoogle Scholar
  13. 13.
    Euzenat, J., Shvaiko, P. (eds.): Ontology Matching, vol. 18. Springer-Verlag New York Inc., New York (2007)zbMATHGoogle Scholar
  14. 14.
    Hanif, M.S., Aono, M.: An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size. J. Web Semant. 7(4), 344–356 (2009)CrossRefGoogle Scholar
  15. 15.
    Hong-Minh, T., Smith, D.: Hierarchical approach for datatype matching in xml schemas. In: 24th British National Conference on Databases, pp. 120–129 (2007)Google Scholar
  16. 16.
    Hu, W., Qu, Y., Cheng, G.: Matching large ontologies: a divide-and-conquer approach. Data Knowl. Eng. 67(1), 140–160 (2008)CrossRefGoogle Scholar
  17. 17.
    Jean-Mary, Y.R., Shironoshita, E.P., Kabuka, M.R.: Ontology matching with semantic verification. Web Semant. 7(3), 235–251 (2009)CrossRefGoogle Scholar
  18. 18.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of Conference on Research in Computational Linguistics, pp. 1–15 (1997)Google Scholar
  19. 19.
    Jiang, S., Lowd, D., Dou, D.: Ontology matching with knowledge rules. CoRR, abs/1507.03097 (2015)Google Scholar
  20. 20.
    Lambrix, P., Tan, H.: Sambo-a system for aligning and merging biomedical ontologies. Web Semant. 4(3), 196–206 (2006)CrossRefGoogle Scholar
  21. 21.
    Li, J., Tang, J., Li, Y., Luo, Q.: RiMOM: a dynamic multistrategy ontology alignment framework. Trans. Knowl. Data Eng. 21(8), 1218–1232 (2009)CrossRefGoogle Scholar
  22. 22.
    Mukkala, L., Arvo, J., Lehtonen, T., Knuutila, T., et al.: Current state of ontology matching. A survey of ontology and schema matching. Technical report 4, University of Turku, pp. 1–18 (2015)Google Scholar
  23. 23.
    Nayak, R., Tran, T.: A progressive clustering algorithm to group the XML data by structural and semantic similarity. Int. J. Pattern Recogn. Artif. Intell. 21(04), 723–743 (2007)CrossRefGoogle Scholar
  24. 24.
    Nayak, R., Xia, F.B.: Automatic integration of heterogenous XML-schemas. In: Proceedings of Information Integration and Web Based Appslications & Services, pp. 1–10 (2004)Google Scholar
  25. 25.
    Ngo, D., Bellahsene, Z.: Overview of YAM++(not) yet another matcher for ontology alignment task. Web Semant.: Sci. Serv. Agents WWW 41, 30–49 (2016)CrossRefGoogle Scholar
  26. 26.
    Stoilos, G., Stamou, G., Kollias, S.: A string metric for ontology alignment. In: Proceedings of International Conference on the SW, pp. 624–637 (2005)Google Scholar
  27. 27.
    Thang, H.Q., Nam, V.S.: Xml schema automatic matching solution. Comput. Electr. Autom. Control Inf. Eng. 4(3), 456–462 (2010)Google Scholar
  28. 28.
    Thuy, P.T., Lee, Y.-K., Lee, S.: Semantic and structural similarities between XML schemas for integration of ubiquitous healthcare data. Pers. Ubiquitous Comput. 17(7), 1331–1339 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Irvin Dongo
    • 1
  • Firas Al Khalil
    • 2
  • Richard Chbeir
    • 1
  • Yudith Cardinale
    • 1
    • 3
  1. 1. University Pau & Pays Adour, LIUPPA, EA3000AngletFrance
  2. 2.University College Cork, CRCTCCorkIreland
  3. 3.Departamento de ComputaciónUniversidad Simón BolívarCaracasVenezuela

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