计算机科学
事件(粒子物理)
推论
行人
Softmax函数
图形
依赖关系(UML)
路径(计算)
素描
数据挖掘
人工智能
理论计算机科学
算法
深度学习
物理
工程类
程序设计语言
量子力学
运输工程
作者
Lili Yang,Hang Gao,Hongfei Jia,Qingyu Luo
出处
期刊:International Journal of Modern Physics B
[World Scientific]
日期:2022-12-03
卷期号:37 (16)
标识
DOI:10.1142/s0217979223501527
摘要
An event logic graph is a kind of knowledge mapping technology for knowledge inference and simulation analysis, which takes events as the core and portrays the hierarchical system and logical evolution pattern between events. In order to apply it to further improve the accuracy of related studies, such as pedestrian flow evacuation, simulation model optimization and risk prediction. In this paper, we use social network resources, media resources and journal database resources to build our corpus and adopt the explicit event relationship extraction method based on syntactic dependency and the implicit event relationship extraction method based on BERT+Bi-LSTM+Attention+Softmax for the characteristics of explicit event relationship and implicit event relationship, respectively. This paper constructs a pedestrian flow evacuation matter mapping for three typical scenarios and discusses its application path. It is found that once a sound knowledge base of logical reasoning and event logic graph is established, both research on optimization of pedestrian flow evacuation simulation models and research on identification and assessment of pedestrian flow evacuation safety risks will receive excellent support.
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