  The World Economic Forum (WEF) publishes annual reports on global risks which
have the high impact on the world's economy. Currently, many researchers
analyze the modeling and evolution of risks. However, few studies focus on
validation of the global risk networks published by the WEF. In this paper, we
first create a risk knowledge graph from the annotated risk events crawled from
the Wikipedia. Then, we compare the relational dependencies of risks in the WEF
and Wikipedia networks, and find that they share over 50% of their edges.
Moreover, the edges unique to each network signify the different perspectives
of the experts and the public on global risks. To reduce the cost of manual
annotation of events triggering risk activation, we build an auto-detection
tool which filters out over 80% media reported events unrelated to the global
risks. In the process of filtering, our tool also continuously learns keywords
relevant to global risks from the event sentences. Using locations of events
extracted from the risk knowledge graph, we find characteristics of
geographical distributions of the categories of global risks.
