基于规则的城市轨道交通安全事件信息抽取及其知识元表示

合理利用城市轨道交通安全事件案例对突发事件下辅助制定应急决策具有重大意义.目前,中国轨道交通运营商存储了大量的安全事件案例,但大多以自由化或半自由化文本的形式存储在数据库中,使用率较低.为提高城市轨道交通安全事件案例的使用效率,提出了基于规则的信息抽取方法,将城市轨道交通安全事件案例的自由文本转化为用共性知识元表示的结构化数据.通过指标计算,所提出的方法可以以较高的准确率和召回率对安全事件要素进行抽取,可为突发事故时的应急决策制定提供高效的数据支撑.

Research on Rule-based Information Extraction of Urban Rail Transit Safety Cases and Its Common Knowledge Meta-model Representation

It is of great significance to assist in emergency decision-making by making use of urban rail transit safety event cases reasonably. At present, a large number of security cases are stored by China''s rail transit operators store, but most of them are stored in the database in the form of free or semi-free texts, with low utilization rate. In order to improve the use efficiency of urban rail transit safety cases, this paper proposed a rule-based information extraction method. It converted the free texts of urban rail transit safety cases into structured data represented by common knowledge elements. Through the index calculation, the elements of safety events can be extracted by the method proposed in this paper with high accuracy and recall rate. And efficient data support for emergency decision-making can be provided.