Ontology-Based Semantic Checking of Data in Railway Infrastructure Information Systems

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Foundations of Computing and Decision Sciences Pub Date : 2022-09-01 DOI:10.2478/fcds-2022-0016
V. Shynkarenko, L. Zhuchyi, Oleksandr Ivanov
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Abstract

Abstract Semantic checking of railway infrastructure information support data is one of the ways to improve the consistency of information system data and, as a result, increase the safety of train traffic. Existing ontological developments have demonstrated the applicability of description logic for modelling railway transport, but have not paid enough attention to the data resources structure and the railway regulatory support. In this work, the formalization of the tabular presentation of data and the rules of railway transport regulations is carried out using the example of a connection track passport and temporary speed restrictions using ontological means, data wrangling and extraction tools. Ontologies of the various formats data resources and railway station infrastructure, tools for converting and extracting data have been developed. The semantic checking of the compliance of railway information system data with regulatory documents in terms of the connection track passport is carried out on the basis of a multi-level concretization model and integration of ontologies. The mechanisms for implementing the constituent ontologies and their integration are demonstrated by an example. Further research includes ontological checking of natural language normative documents of railway transport.
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基于本体的铁路基础设施信息系统数据语义校验
摘要对铁路基础设施信息支持数据进行语义检查是提高信息系统数据一致性,从而提高列车行车安全性的途径之一。现有本体论的发展已经证明了描述逻辑对铁路运输建模的适用性,但对数据资源结构和铁路监管支持的重视不够。在这项工作中,使用连接轨道护照和临时速度限制的例子,使用本体论手段、数据争用和提取工具,对数据的表格表示和铁路运输法规的规则进行了形式化。各种格式数据资源和火车站基础设施的本体,转换和提取数据的工具已经开发出来。基于多层次的具体化模型和本体集成,对铁路信息系统数据与规范性文件在连接轨道通行证方面的符合性进行语义检验。通过一个示例演示了实现组成本体及其集成的机制。进一步的研究包括对铁路运输自然语言规范性文件的本体论检验。
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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