CauseNet

Towards a Causality Graph Extracted from the Web

verfasst von

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

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.

Externe Organisation(en)

Universität Paderborn
Technische Universität München (TUM)
Universität Leipzig

Typ

Aufsatz in Konferenzband

Seiten

3023-3030

Anzahl der Seiten

8

Publikationsdatum

19.10.2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Betriebswirtschaft, Management und Rechnungswesen (insg.), Entscheidungswissenschaften (insg.)

Elektronische Version(en)

https://doi.org/10.1145/3340531.3412763 (Zugang: Geschlossen)