Details
| Original language | English |
|---|---|
| Title of host publication | CIKM' 2020 |
| Subtitle of host publication | Proceedings of the 29th ACM International Conference on Information and Knowledge Management |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 3023-3030 |
| Number of pages | 8 |
| ISBN (print) | 9781450368599 |
| Publication status | Published - 19 Oct 2020 |
| Externally published | Yes |
| Event | 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - online, Virtual, Online, Ireland Duration: 19 Oct 2020 → 23 Oct 2020 |
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.
Keywords
- causality, information extraction, knowledge graph
ASJC Scopus subject areas
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CIKM' 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management. New York: Association for Computing Machinery (ACM), 2020. p. 3023-3030.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - CauseNet
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
AU - Heindorf, Stefan
AU - Scholten, Yan
AU - Wachsmuth, Henning
AU - Ngonga Ngomo, Axel Cyrille
AU - Potthast, Martin
PY - 2020/10/19
Y1 - 2020/10/19
N2 - 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.
AB - 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.
KW - causality
KW - information extraction
KW - knowledge graph
UR - http://www.scopus.com/inward/record.url?scp=85095862839&partnerID=8YFLogxK
U2 - 10.1145/3340531.3412763
DO - 10.1145/3340531.3412763
M3 - Conference contribution
AN - SCOPUS:85095862839
SN - 9781450368599
SP - 3023
EP - 3030
BT - CIKM' 2020
PB - Association for Computing Machinery (ACM)
CY - New York
Y2 - 19 October 2020 through 23 October 2020
ER -