Advertisement

Detect Redundant RDF Data by Rules

  • Tao GuangEmail author
  • Jinguang Gu
  • Li Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9645)

Abstract

The development and standardization of semantic web technologies have resulted in an unprecedented volume of RDF datasets being published on the Web. However, data quality exists in most of the information systems, and the RDF data is no exception. The quality of RDF data has become a hot spot of Web research and many data quality dimensions and metrics have been proposed. In this paper, we focus on the redundant problem in RDF data, and propose a rule based method to find and delete the semantic redundant triples. By evaluating the existing datasets, we prove that our method can remove the redundant triples to help data publisher provide more concise RDF data.

Keywords

RDF Data quality Semantic redundancy Rule 

Notes

Acknowledgement

This work was partially supported by a grant from the NSF (Natural Science Foundation) of China under grant number 60803160 and 61272110, the Key Projects of National Social Science Foundation of China under grant number 11&ZD189, and it was partially supported by a grant from NSF of Hubei Prov. of China under grant number 2013CFB334. It was partially supported by NSF of educational agency of Hubei Prov. under grant number Q20101110, and the State Key Lab of Software Engineering Open Foundation of Wuhan University under grant number SKLSE2012-09-07.

References

  1. 1.
    Hayes, P.: RDF semantics. Technical report, W3C. W3C recommendation, February 2014. http://www.w3.org/TR/2014/REC-rdf11-mt-20140225/
  2. 2.
    W3C Data Activity. http://www.w3.org/2013/data/
  3. 3.
    Bizer, C., Paulheim, H.: State of the LOD Cloud 2014 (2014)Google Scholar
  4. 4.
    Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment methodologies for linked open data. Semantic Web (2013)Google Scholar
  5. 5.
    Acosta, M., Zaveri, A., Simperl, E., Kontokostas, D., Auer, S., Lehmann, J.: Crowdsourcing linked data quality assessment. In: Alani, H., et al. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 260–276. Springer, Heidelberg (2013)Google Scholar
  6. 6.
    Mendes, P.N., Bizer, C., Young J.H., Miklos, Z., Calbimonte J.P., Moraru, A.: Conceptual model and best practices for high-quality metadata. Delivery 2.1 of PlanetData, FP7 project 257641 (2012)Google Scholar
  7. 7.
    Fernández, J.D., Martínez-Prieto, M.A., Gutiérrez, C., Polleres, A., Arias, M.: Binary RDF representation for publication and exchange (HDT). Web Semant. Sci. Serv. Agents World Wide Web 19, 22–41 (2013)CrossRefGoogle Scholar
  8. 8.
    Lvarez-García, S., Brisaboa, N.R., Fernández, J.D., Martínez-Prieto, M.A.: Compressed k2-triples for full-in-memory RDF engines. ArXiv preprint (2011)Google Scholar
  9. 9.
    Motik, B., Grau, B.C., Horrocks, I., Wu, Z., Fokoue, A., Lutz, C.: OWL 2 Web ontology language profiles, 2nd edn. W3C Recommendation (December 2012)Google Scholar
  10. 10.
    Pichler, R., Polleres, A., Skritek, S., Woltran, S.: Redundancy elimination on RDF graphs in the presence of rules, constraints, and queries. In: Hitzler, P., Lukasiewicz, T. (eds.) RR 2010. LNCS, vol. 6333, pp. 133–148. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhanChina

Personalised recommendations