School of Information Science and Technology, Qingdao University of Science and Technology, Qicngdao 266061, China.
Comput Intell Neurosci. 2020 Jun 19;2020:7132072. doi: 10.1155/2020/7132072. eCollection 2020.
Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents. However, it is a complicated work to find causality among events in a CEEG. This paper presents a method to accurately extract event causality by using a neural network and structural analysis. First, we identify the events and their component elements from fault trees by natural language processing technology. Then, causality in accident events is divided into explicit causality and implicit causality. Explicit causality is obtained by analyzing the hierarchical structure relations of event nodes and the semantics of component logic gates in fault trees. By integrating internal structural features of events and semantic features of event sentences, we extract implicit causality by utilizing a bidirectional gated recurrent unit (BiGRU) neural network. An algorithm, named CEFTAR, is presented to extract causality for safety events in chemical accidents from fault trees and accident reports. Compared with the existing methods, experimental results show that our method has a higher accuracy and recall rate in extracting causality.
化学事件演化图 (CEEG) 是进行安全分析、预警和紧急处理化学事故的有效工具。然而，在 CEEG 中找到事件之间的因果关系是一项复杂的工作。本文提出了一种使用神经网络和结构分析准确提取事件因果关系的方法。首先，我们通过自然语言处理技术从故障树中识别事件及其组成元素。然后，将事故事件中的因果关系分为显式因果关系和隐式因果关系。显式因果关系是通过分析事件节点的层次结构关系和故障树中组件逻辑门的语义获得的。通过整合事件的内部结构特征和事件句子的语义特征，我们利用双向门控循环单元 (BiGRU) 神经网络提取隐式因果关系。提出了一种名为 CEFTAR 的算法，用于从故障树和事故报告中提取化学事故安全事件的因果关系。与现有方法相比，实验结果表明，我们的方法在提取因果关系方面具有更高的准确性和召回率。
