Interactive Method to Elicit Local Causal Knowledge for Creating a Huge Causal Network

Abstract

Effective disaster drills and exercises require appropriate scenarios reflecting concrete disaster situations. It is however not easy to manually create such a scenario with enough details and validity, because it is fundamental difficult to comprehensively predict and assume disaster situations that may occur in various phases through a chain of causality from the primary damage. In order to make a scenario creation easier and more efficient, some support tools are necessary, in particular for predicting what kind of situations will happen through a causal chain from a base disaster assumption. In this paper, we proposed a simple and practical causal model consisting of three elements: cause, precondition, and effect, which can capture indirect causal relationships between two events by introducing the concept of preconditions. We also developed an interactive method with a GUI to elicit causal knowledge about disaster situations based on the model. Users can enter possible events that can occur in a disaster as well as countermeasures against those events by answering the questions presented on the GUI. Then the entered sentences are processed to identify causal elements automatically by a newly developed NLP techniques, and finally those elements are integrated into the database. The proposed method still has a room for improvement, however its performance is satisfying and can be expected to be utilized as a technical base for the creation of effective disaster scenarios.