Dataset Open Access

CauseNet: Towards a Causality Graph Extracted from the Web

Heindorf, Stefan; Scholten, Yan; Wachsmuth, Henning; Ngonga Ngomo, Axel-Cyrille; Potthast, Martin

Causal knowledge is seen as one of the key ingredients to advance artificial intelligence. Yet, few knowledge bases comprise causal knowledge to date, possibly due to significant efforts required for validation. Notwithstanding this challenge, we compile CauseNet, a large-scale knowledge base of claimed causal relations between causal concepts. By extraction from different semi- and unstructured web sources, we collect more than 11 million causal relations with an estimated extraction precision of 83% and construct the first large-scale and open-domain causality graph. We analyze the graph to gain insights about causal beliefs expressed on the web and we demonstrate its benefits in basic causal question answering. Future work may use the graph for causal reasoning, computational argumentation, multi-hop question answering, and more.

When using the data, please make sure to refer to it as follows:

@inproceedings{heindorf2020causenet,
  author    = {Stefan Heindorf and
               Yan Scholten and
               Henning Wachsmuth and
               Axel-Cyrille Ngonga Ngomo and
               Martin Potthast},
  title     = {CauseNet: Towards a Causality Graph Extracted from the Web},
  booktitle = {{CIKM}},
  pages     = {3023--3030},
  publisher = {{ACM}},
  year      = {2020}
}

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causenet-sample.json
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  • Stefan Heindorf, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast. CauseNet: Towards a Causality Graph Extracted from the Web. In CIKM 2020, pages 2023-3030. ACM.

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