WebIsALOD: Providing Hypernymy Relations Extracted from the Web as Linked Open Data

  • Sven HertlingEmail author
  • Heiko PaulheimEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10588)


Hypernymy relations are an important asset in many applications, and a central ingredient to Semantic Web ontologies. The IsA database is a large collection of such hypernymy relations extracted from the Common Crawl. In this paper, we introduce WebIsALOD, a Linked Open Data release of the IsA database, containing 400M hypernymy relations, each provided with rich provenance information. As the original dataset contained more than 80% wrong, noisy extractions, we run a machine learning algorithm to assign confidence scores to the individual statements. Furthermore, 2.5M links to DBpedia and 23.7k links to the YAGO class hierarchy were created at a precision of 97%. In total, the dataset contains 5.4B triples.


Hypernyms Hearst patterns Linked dataset 


  1. 1.
    Bryl, V., Bizer, C., Paulheim, H.: Gathering alternative surface forms for DBpedia entities. In: NLP-DBPEDIA@ISWC, pp. 13–24 (2015)Google Scholar
  2. 2.
    Carroll, J.J., Bizer, C., Hayes, P., Stickler, P.: Named graphs, provenance and trust. In: International Conference on World Wide Web, pp. 613–622. ACM (2005)Google Scholar
  3. 3.
    Hauser, D.J., Schwarz, N.: Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behav. Res. Methods 48(1), 400–407 (2016)CrossRefGoogle Scholar
  4. 4.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification (2003)Google Scholar
  5. 5.
    Kazai, G.: In search of quality in crowdsourcing for search engine evaluation. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 165–176. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-20161-5_17 CrossRefGoogle Scholar
  6. 6.
    Kliegr, T., Zamazal, O.: LHD 2.0: a text mining approach to typing entities in knowledge graphs. Web Semant.: Sci. Serv. Agents World Wide Web 39, 47–61 (2016)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: 7th International Conference on Semantic Systems, pp. 1–8. ACM (2011)Google Scholar
  9. 9.
    Paulheim, H., Fümkranz, J.: Unsupervised generation of data mining features from linked open data. In: 2nd International Conference on Web Intelligence, Mining and Semantics, p. 31. ACM (2012)Google Scholar
  10. 10.
    Ringler, D., Paulheim, H.: One knowledge graph to rule them all? Analyzing the differences between DBpedia, Yago, Wikidata & Co. In: 40th German Conference on Artificial Intelligence (2017)Google Scholar
  11. 11.
    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
  12. 12.
    Seitner, J., Bizer, C., Eckert, K., Faralli, S., Meusel, R., Paulheim, H., Ponzetto, S.: A large database of hypernymy relations extracted from the web. In: Language Resources and Evaluation Conference, Portoroz, Slovenia (2016)Google Scholar
  13. 13.
    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

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Data and Web Science GroupUniversity of MannheimMannheimGermany

Personalised recommendations