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.
%0 Conference Paper
%1 10.1145/3340531.3412763
%A Heindorf, Stefan
%A Scholten, Yan
%A Wachsmuth, Henning
%A Ngonga Ngomo, Axel-Cyrille
%A Potthast, Martin
%B Proceedings of the 29th ACM International Conference on Information & Knowledge Management
%C New York, NY, USA
%D 2020
%I Association for Computing Machinery
%K causality extraction, graph, information knowledge
%P 3023–3030
%R 10.1145/3340531.3412763
%T CauseNet: Towards a Causality Graph Extracted from the Web
%U https://doi.org/10.1145/3340531.3412763
%X 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.
%@ 9781450368599
@inproceedings{10.1145/3340531.3412763,
abstract = {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.},
added-at = {2024-10-02T10:38:17.000+0200},
address = {New York, NY, USA},
author = {Heindorf, Stefan and Scholten, Yan and Wachsmuth, Henning and Ngonga Ngomo, Axel-Cyrille and Potthast, Martin},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2268528f465c7e0ec6e099306d60a8856/scadsfct},
booktitle = {Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
doi = {10.1145/3340531.3412763},
interhash = {a0784d54b187dd73320ab4f6922fc324},
intrahash = {268528f465c7e0ec6e099306d60a8856},
isbn = {9781450368599},
keywords = {causality extraction, graph, information knowledge},
location = {Virtual Event, Ireland},
numpages = {8},
pages = {3023–3030},
publisher = {Association for Computing Machinery},
series = {CIKM '20},
timestamp = {2024-10-02T10:38:17.000+0200},
title = {CauseNet: Towards a Causality Graph Extracted from the Web},
url = {https://doi.org/10.1145/3340531.3412763},
year = 2020
}
