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One Knowledge Graph to Rule Them All? Analyzing the Differences Between DBpedia, YAGO, Wikidata & co.

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KI 2017: Advances in Artificial Intelligence (KI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10505))

Abstract

Public Knowledge Graphs (KGs) on the Web are considered a valuable asset for developing intelligent applications. They contain general knowledge which can be used, e.g., for improving data analytics tools, text processing pipelines, or recommender systems. While the large players, e.g., DBpedia, YAGO, or Wikidata, are often considered similar in nature and coverage, there are, in fact, quite a few differences. In this paper, we quantify those differences, and identify the overlapping and the complementary parts of public KGs. From those considerations, we can conclude that the KGs are hardly interchangeable, and that each of them has its strenghts and weaknesses when it comes to applications in different domains.

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Notes

  1. 1.

    https://googleblog.blogspot.de/2012/05/introducing-knowledge-graph-things-not.html.

  2. 2.

    https://www.wikidata.org/wiki/Wikidata:Data_donation.

  3. 3.

    Freebase was discarded as it is discontinued, and non-public KGs were not considered, as it is impossible to run the analysis on non-public data.

  4. 4.

    Scripts are available at https://github.com/dringler/KnowledgeGraphAnalysis.

  5. 5.

    The reason why so few politicians, actors, and athletes are listed for Wikidata is that they are usually not modeled using explicit classes.

  6. 6.

    Note that it is not necessary that the linking approach is particularly good, as long as we can estimate its quality reasonably well. In our experiments, the agreement about the estimated overlap is rather high, showing an intra-class correlation coefficient (ICC) of 0.969. In contrast, the size of the actual alignments found by the different approaches differs a lot more, showing an ICC of only 0.646.

References

  1. Carlson, A., Betteridge, J., Wang, R.C., Hruschka Jr., E.R., Mitchell, T.M.: Coupled semi-supervised learning for information extraction. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 101–110 (2010)

    Google Scholar 

  2. Färber, M., Ell, B., Menne, C., Rettinger, A., Bartscherer, F.: Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semant. Web (2016, to appear)

    Google Scholar 

  3. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia-A large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web J. 6(2), 167–195 (2013)

    Google Scholar 

  4. Lenat, D.B.: CYC: a large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 33–38 (1995)

    Article  Google Scholar 

  5. Nentwig, M., Hartung, M., Ngonga Ngomo, A.C., Rahm, E.: A survey of current link discovery frameworks. Semant. Web 8(3), 419–436 (2017)

    Article  Google Scholar 

  6. Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)

    Article  Google Scholar 

  7. Pellissier Tanon, T., Vrandečić, D., Schaffert, S., Steiner, T., Pintscher, L.: From Freebase to Wikidata: the great migration. In: Proceedings of the 25th International Conference on World Wide Web, pp. 1419–1428 (2016)

    Google Scholar 

  8. Schmachtenberg, M., Bizer, C., Paulheim, H.: Adoption of the linked data best practices in different topical domains. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 245–260. Springer, Cham (2014). doi:10.1007/978-3-319-11964-9_16

    Google Scholar 

  9. Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge unifying WordNet and Wikipedia. In: 16th International Conference on World Wide Web, pp. 697–706 (2007)

    Google Scholar 

  10. Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledge base. Commun. ACM 57(10), 78–85 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Daniel Ringler & Heiko Paulheim

Authors
  1. Daniel Ringler
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  2. Heiko Paulheim
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Correspondence to Heiko Paulheim .

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Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany

    Gabriele Kern-Isberner

  2. FB Informatik, TU Darmstadt, Darmstadt, Hessen, Germany

    Johannes Fürnkranz

  3. FB Informatik, Universität Koblenz, Koblenz, Rheinland-Pfalz, Germany

    Matthias Thimm

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Ringler, D., Paulheim, H. (2017). 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: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_33

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  • DOI: https://doi.org/10.1007/978-3-319-67190-1_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67189-5

  • Online ISBN: 978-3-319-67190-1

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