2025年度 人工知能学会全国大会（第39回）
2025年5月27日〜5月30日大阪国際会議場＋オンライン
2025年度 人工知能学会全国大会（第39回）
2025年5月27日〜5月30日大阪国際会議場＋オンライン
[4K3-IS-2f-01]Unraveling Global-to-Domain Events Influence based on Causal Graph
〇Ziwei Xu1, Ryutaro Ichise2,1(1. National Institute of Advanced Industrial Science and Technology, 2. Institute of Science Tokyo)
Global events can significantly impact various sectors but detail the impacts from global events to a specific business remains complex. If these impacts could be represented explicitly, it will enhance event prediction across various domains and help to avoid risk. This paper addresses this challenge within the finance sector by proposing a method that uses causal graphs to link global events with company-specific business events through geographical and taxonomic mappings. We outline a framework encompassing event acquisition, geographic mapping, and event taxonomic mapping, and illustrate its application with an example, demonstrating how geographical and ontological factors reveal hidden influences on business events.