  Many financial jobs rely on news to learn about causal events in the past and
present, to make informed decisions and predictions about the future. With the
ever-increasing amount of news available online, there is a need to automate
the extraction of causal events from unstructured texts. In this work, we
propose a methodology to construct causal knowledge graphs (KGs) from news
using two steps: (1) Extraction of Causal Relations, and (2) Argument
Clustering and Representation into KG. We aim to build graphs that emphasize on
recall, precision and interpretability. For extraction, although many earlier
works already construct causal KGs from text, most adopt rudimentary
pattern-based methods. We close this gap by using the latest BERT-based
extraction models alongside pattern-based ones. As a result, we achieved a high
recall, while still maintaining a high precision. For clustering, we utilized a
topic modelling approach to cluster our arguments, so as to increase the
connectivity of our graph. As a result, instead of 15,686 disconnected
subgraphs, we were able to obtain 1 connected graph that enables users to infer
more causal relationships from. Our final KG effectively captures and conveys
causal relationships, validated through experiments, multiple use cases and
user feedback.
