Learning Semantic N-Ary Relations from Wikipedia

  • Marko Banek
  • Damir Jurić
  • Zoran Skočir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6261)


Automated construction of ontologies from text corpora, which saves both time and human effort, is a principal condition for realizing the idea of the Semantic Web. However, the recently proposed automated techniques are still limited in the scope of context that can be captured. Moreover, the source corpora generally lack the consensus of ontology users regarding the understanding and interpretation of ontology concepts. In this paper we introduce an unsupervised method for learning domain n-ary relations from Wikipedia articles, thus harvesting the consensus reached by the largest world community engaged in collecting and classifying knowledge. Providing ontologies with n-ary relations instead of the standard binary relations built on the subject-verb-object paradigm results in preserving the initial context of time, space, cause, reason or quantity that otherwise would be lost irreversibly. Our preliminary experiments with a prototype software tool show highly satisfactory results when extracting ternary and quaternary relations, as well as the traditional binary ones.


Binary Relation Text Corpus Prepositional Phrase Ontology Learning Prepositional Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marko Banek
    • 1
  • Damir Jurić
    • 1
  • Zoran Skočir
    • 1
  1. 1.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

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