Constructive-Synthesizing Modelling of Ontological Document Management Support for the Railway Train Speed Restrictions
DOI:
https://doi.org/10.15802/stp2022/268001Keywords:
constructive-synthesizing modelling, ontology, information system, railway, database, table, speed restriction, ontology learningAbstract
Purpose. During the development of railway ontologies, it is necessary to take into account both the data of information systems and regulatory support to check their consistency. To do this, data integration is performed. The purpose of the work is to formalize the methods for integrating heterogeneous sources of information and ontology formation. Methodology. Constructive-synthesizing modelling of ontology formation and its resources was developed. Findings. Ontology formation formalization has been performed, which allows expanding the possibilities of automating the integration and coordination of data using ontologies. In the future, it is planned to expand the structural system for the formation of ontologies based on textual sources of railway regulatory documentation and information systems. Originality. The authors laid the foundations of using constructive-synthesizing modelling in the railway transport ontological domain to form the structure and data of the railway train speed restriction warning tables (database and csv format), their transformation into a common tabular format, vocabulary, rules and ontology individuals, as well as ontology population. Ontology learning methods have been developed to integrate data from heterogeneous sources. Practical value. The developed methods make it possible to integrate heterogeneous data sources (the structure of the table of the railway train management rules, the form and application for issuing a warning), which are railway domain-specific. It allows forming an ontology from its data sources (database and csv formats) to schema and individuals. Integration and consistency of information system data and regulatory documentation is one of the aspects of increasing the level of train traffic safety.
References
Amardeilh, F. (2006). OntoPop or how to annotate documents and populate ontologies from texts. ESWC 2006 Workshop on Mastering the Gap: From Information Extraction to Semantic Representation, 1-16. (in English)
An, J., & Park, Y. B. (2018). Methodology for Automatic Ontology Generation Using Database Schema Information. Mobile Information Systems, 2018, 1-13. DOI: https://doi.org/10.1155/2018/1359174 (in English)
Bischof, S., & Schenner, G. (2021). Rail Topology Ontology: A Rail Infrastructure Base Ontology. In The semantic web - ISWC 2021 (pp. 597-612). DOI: https://doi.org/10.1007/978-3-030-88361-4_35 (in English)
Cimiano, P., & Völker, J. (2005). A Framework for Ontology Learning and Data-Driven Change Discovery. In NLDB'05: Proceedings of the 10th international conference on Natural Language Processing and Information Systems (Vol. 3513, pp. 227-238). DOI: https://doi.org/10.1007/11428817_21 (in English)
Crispino, G. (2003). Une Plateforme Informatique de l'Exploration Contextuelle: Modelisation, Architecture et Realisation (ContextO). Application au Filtrage Semantique de Textes (PhD dissertation). Universite De Paris Iv –Sorbonne. (in French)
Cruz, C., & Nicolle, N. (2008). Ontology enrichment and automatic population from XML data. In Proceedings of the 4th International VLDB Workshop on Ontology-based Techniques for DataBases in Information Systems and Knowledge Systems (pp. 1-5). (in English)
Dinşoreanu, M., Salomie, I., & Pop, C. B. (2011). Integrated System for Developing Semantically-Enhanced Archive Econtent. Revista Romana de Informatica si Automatică, 21(4), 67-77. (in English)
Fonseca, J. M. S. (2014). Converting ontologies into DSLs (PhD dissertation). Universidade do Minho Escola de Engenharia Departamento de Inform´atica. (in English)
Klie, J. C., Bugert, M., Boullosa, B., de Castilho, R. E., & Gurevych, I. (2018). The inception platform: Machine-assisted and knowledge-oriented interactive annotation. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations (pp. 5-9). (in English)
Knoblock, C. A., & Szekely, P. (2015). Exploiting semantics for big data integration. AI magazine, 36(1), 25-38. DOI: https://doi.org/10.1609/aimag.v36i1.2565 (in English)
Ma, C., & Molnar, B. (2022). Ontology Learning from Relational Database: Opportunities for Semantic Information Integration. Vietnam Journal of Computer Science, 9(1), 31-57. DOI: https://doi.org/10.1142/s219688882150024x (in English)
Quirino, G. K. S., Barcellos, M. P., & Falbo, R. A. (2017). OPL-ML: A Modeling Language for Representing On-tology Pattern Languages. In Lecture Notes in Computer Science (pp. 187-201). DOI: https://doi.org/10.1007/978-3-319-70625-2_18 (in English)
Ruy, F. B., Guizzardi, G., Falbo, R. A., Reginato, C. C., & Santos, V. A. (2017). From reference ontologies to on-tology patterns and back. Data & Knowledge Engineering, 109, 41-69. DOI: https://doi.org/10.1016/j.datak.2017.03.004 (in English)
Tilakaratna, P., & Rajapakse, J. (2012). Conceptual and System Modeling with UML: Guidelines. International Journal of Digital Content Technology and its Applications, 6(22), 90-97. DOI: https://doi.org/10.4156/jdcta.vol6.issue22.9 (in English)
Tutcher, J. (2016). Development of semantic data models to support data interoperability in the rail industry (PhD dissertation). University of Birmingham. (in English)
Shynkarenko, V., & Ilman, V. M. (2014). Constructive-synthesizing structures and their grammatical interpreta-tions. i. Generalized formal constructive-synthesizing structure. Cybernetics and Systems Analysis, 50(5), 655-662. DOI: https://doi.org/10.1007/s10559-014-9655-z (in English)
Shynkarenko, V., Zhuchyi, L., & Ivanov, O. (2021). Conceptualization of the tabular representation of knowledge. In 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (pp. 248-251). DOI: https://doi.org/10.1109/CSIT52700.2021.9648761 (in English)
Shynkarenko, V., & Zhuchyi, L. Constructive-synthesizing modeling of ontological document management support for the railway train speed restrictions. (n. d.). URL: https://tinyurl.com/2p9eeand (in English)
Ukrainian railway train management rules. (2005). URL: https://zakon.rada.gov.ua/rada/show/v0507650-05#Text (in English)
Ukrainian railway train speed restriction form. URL: https://images.app.goo.gl/mgrtncgyDKTsHbYF7 (in English)
Xiao, G., Lanti, D., Kontchakov, R., Komla- Ebri, S., Güzel-Kalaycı, E., Ding, L., … & Botoeva, E. (2020). The Virtual Knowledge Graph System Ontop. Lecture Notes in Computer Science, 259-277. DOI: https://doi.org/10.1007/978-3-030-62466-8_17 (in English)
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Science and Transport Progress
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright and Licensing
This journal provides open access to all of its content.
As such, copyright for articles published in this journal is retained by the authors, under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). The CC BY license permits commercial and non-commercial reuse. Such access is associated with increased readership and increased citation of an author's work. For more information on this approach, see the Public Knowledge Project, the Directory of Open Access Journals, or the Budapest Open Access Initiative.
The CC BY 4.0 license allows users to copy, distribute and adapt the work in any way, provided that they properly point to the author. Therefore, the editorial board of the journal does not prevent from placing published materials in third-party repositories. In order to protect manuscripts from misappropriation by unscrupulous authors, reference should be made to the original version of the work.