Skip to main content
View expanded cover

European Semantic Web Conference

ESWC 2018: The Semantic Web: ESWC 2018 Satellite Events pp 72–76Cite as

Exploiting Equivalence to Infer Type Subsumption in Linked Graphs

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11155)

Abstract

Open Knowledge Graphs (KGs) such as DBpedia and Wikidata have been recognized as the foundations for diverse applications in the field of data mining and information retrieval. Each of these KGs follows a different knowledge organization as well as is based on differently structured ontologies. Moreover, it has been observed that type information are often noisy, incomplete or even incorrect. In general, there is a need for well defined and comparable type information for the entities of the KGs. In this paper, we propose an isomorphism-based approach to infer subsumption relations to RDF type information in Wikidata by exploiting the RDF type information from DBpedia.

Keywords

  • Knowledge graph
  • RDF
  • Wikidata
  • DBpedia

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-98192-5_14
  • Chapter length: 5 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   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-98192-5
  • 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   89.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://www.wikidata.org/wiki/Property:P31.

  2. 2.

    https://www.w3.org/RDFS/.

References

  1. Directory with all achieved results. https://github.com/ISE-AIFB/Wiki_DB

  2. Färber, M., Bartscherer, F., Menne, C., Rettinger, A.: Linked data quality of DBpedia, Freebase, Opencyc, Wikidata, and YAGO. Semant. Web 9(1), 77–129 (2018)

    CrossRef  Google Scholar 

  3. Fleischhacker, D., Paulheim, H., Bryl, V., Völker, J., Bizer, C.: Detecting errors in numerical linked data using cross-checked outlier detection. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 357–372. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11964-9_23

    CrossRef  Google Scholar 

  4. Ismayilov, A., Kontokostas, D., Auer, S., Lehmann, J., Hellmann, S.: Wikidata through the Eyes of DBpedia. CoRR abs/1507.04180 (2015)

    Google Scholar 

  5. Koutraki, M., Preda, N., Vodislav, D.: Online relation alignment for linked datasets. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10249, pp. 152–168. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58068-5_10

    CrossRef  Google Scholar 

  6. Melo, A., Paulheim, H., Völker, J.: Type prediction in RDF knowledge bases using hierarchical multilabel classification. In: WIMS, p. 14 (2016)

    Google Scholar 

  7. Paulheim, H., Bizer, C.: Type inference on noisy RDF data. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 510–525. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_32

    CrossRef  Google Scholar 

  8. Ringler, D., Paulheim, H.: One knowledge graph to rule them all? Analyzing the differences between DBpedia, YAGO, Wikidata & co. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds.) KI 2017. LNCS, vol. 10505, pp. 366–372. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67190-1_33

    CrossRef  Google Scholar 

  9. Wienand, D., Paulheim, H.: Detecting incorrect numerical data in DBpedia. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 504–518. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_34

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Karlsruhe, Germany

    Russa Biswas, Maria Koutraki & Harald Sack

  2. Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Russa Biswas, Maria Koutraki & Harald Sack

Authors
  1. Russa Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Koutraki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harald Sack
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Russa Biswas .

Editor information

Editors and Affiliations

  1. University of Bologna, Bologna, Italy

    Dr. Aldo Gangemi

  2. IBM Research - Almaden, San Jose, California, USA

    Anna Lisa Gentile

  3. CNR-ISTC, Rome, Italy

    Andrea Giovanni Nuzzolese

  4. Technische Universität Dresden, Dresden, Germany

    Dr. Sebastian Rudolph

  5. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Dr. Maria Maleshkova

  6. University of Mannheim, Mannheim, Germany

    Heiko Paulheim

  7. University of Aberdeen, Aberdeen, United Kingdom

    Prof. Jeff Z Pan

  8. CNR-ISTC, Rome, Italy

    Mehwish Alam

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Biswas, R., Koutraki, M., Sack, H. (2018). Exploiting Equivalence to Infer Type Subsumption in Linked Graphs. In: , et al. The Semantic Web: ESWC 2018 Satellite Events. ESWC 2018. Lecture Notes in Computer Science(), vol 11155. Springer, Cham. https://doi.org/10.1007/978-3-319-98192-5_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98192-5_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98191-8

  • Online ISBN: 978-3-319-98192-5

  • eBook Packages: Computer ScienceComputer Science (R0)