CauseNet: Towards a Causality Graph Extracted from the Web
DOI10.5281/zenodo.3876154Zenodo3876154MaRDI QIDQ6722944
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 17 October 2020
Copyright license: No records found.
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 claimedcausal 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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