WebChild: Commonsense Knowledge from the Web


WebChild is a large collection of commonsense knowledge, automatically extracted and disambiguated from Web contents. WebChild contains triples that connect nouns with adjectives via fine-grained relations like hasShape, hasTaste, evokesEmotion, etc. The arguments of these assertions, nouns and adjectives, are disambiguated by mapping them onto their proper WordNet senses.

Large-scale experiments demonstrate the high accuracy (more than 80 percent) and coverage (more than four million fine grained disambiguated assertions) of WebChild.


Our method is based on semi-supervised Label Propagation over graphs of noisy candidate assertions. We automatically derive seeds from WordNet and by pattern matching from Web text collections. The Label Propagation algorithm provides us with domain sets and range sets for 19 different relations, and with confidence-ranked assertions between WordNet senses.


wordnet wrappers download 6.3 MB -
part-whole download 6.7 million 89
comparative download 0.81 million 85
property download - -
activity download - -
spatial download - -


  • WebChild 2.0: Fine-Grained Commonsense Knowledge Distillation
    Niket Tandon, Gerard de Melo, Gerhard Weikum
    In: ACL Demo 2017.

  • Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags
    Niket Tandon, Charles Hariman, Jacopo Urbani, Gerhard Weikum, Anna Rohrbach, Marcus Rohrbach
    Accepted: Proc. AAAI 2016.

  • Mining Activity Commonsense Knowledge from Hollywood Narratives
    Niket Tandon, Gerard de Melo, Abir De, Gerhard Weikum
    In: Proc. CIKM 2015.
  • Knowledge Extraction from Movie Scripts
    Niket Tandon, Gerard de Melo, Abir De, Gerhard Weikum
    In: Proc. WWW 2015.
  • Smarter Than You Think: Acquiring Comparative Commonsense from the Web
    Niket Tandon, Gerard de Melo, Gerhard Weikum (2014)
    Accepted: AAAI 2014, Canada.
  • WebChild: Harvesting and Organizing Commonsense Knowledge from the Web
    Niket Tandon, Gerard de Melo, Fabian Suchanek, Gerhard Weikum (2014)
    In: Proc. ACM WSDM 2014, New York City, USA.
    Acceptance rate: 18%.

Linked projects

WebBrain: Joint Neural Learning of Large-Scale Commonsense Knowledge