CauseNet aims at creating a causal knowledge base that comprises all human causal knowledge and to separate it from mere causal beliefs, with the goal of enabling large-scale research into causal inference.
CauseNet: Towards a Causality Graph Extracted from the Web
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.
Download
We provide three versions of our causality graph CauseNet:
CauseNet-Full: The complete dataset
CauseNet-Precision: A subset of CauseNet-Full with higher precision
CauseNet-Sample: A small sample dataset for first explorations and experiments without provenance data
Statistics
Relations Concepts File Size CauseNet-Full 11,609,890 12,186,195 1.8GB CauseNet-Precision 199,806 80,223 135MB CauseNet-Sample 264 524 54KB
... continue reading