Ontological Support for Harmonization and Integration of Ukrzaliznytsia Information Systems Data

L. I Zhuchyi

doi:10.15802/stp2022/265335

Authors

L. I. Zhuchyi Ukrainian State University of Science and Technologies, Ukraine https://orcid.org/0000-0002-9209-7262

DOI:

https://doi.org/10.15802/stp2022/265335

Keywords:

conceptualization, knowledge base, ontology, system analysis, railway

Abstract

Purpose. The development strategy of Ukrzaliznytsia includes the following areas: integration and standardization of information systems, increasing the truthfulness of data and automating business processes. The integration of railway information systems is possible by ontological means without changing their structure. In this work, the main aim is the analysis of existing transportation ontological developments and determination of approaches to the application of related domains developments to the objectives of Ukrzaliznytsia’s development. Methodology. Ontological developments are systematized according to the type and format of their resources, the level of data integration, and the goals of ontology-based software. Methods of system analysis are used. Findings. The analysis showed that European Union railway transport ontologies are used to integrate infrastructure description data, train timetables, and others. At the same time, insufficient attention is paid to the regulatory support of the transportation process. There are software tools for annotating texts, extracting knowledge from tables and developing ontologies, but they are not used to support the Ukrainian railway transportation process. It has been determined that the actual problem is normative documentation annotation to establish a link between the ontology and the regulation texts. Originality. The basis for achieving the development goals of Ukrzaliznytsia by ontological means was laid, using the analysis and systematization of existing ontological developments of transport and related domains. The possibilities of using ontological means in railway transport are scientifically substantiated for: formalization of regulatory support; data transformations; data integration; checking the consistency of information systems data and regulations. Practical value. The work made it possible to identify the most significant ontological projects in transport. The foundations for the implementation of the conceptualization of the tabular representation of knowledge and the development of an ontology for the integration of models of railway subsystems have been laid.

References

Ovcharuk, I., & Boklah, Y. (2020). Information Systems in Rail Transport: Development and Prospects. Digital platform: information technologies in the sociocultural sphere, 3(2), 170-182. DOI: https://doi.org/10.31866/2617-796x.3.2.2020.220594 (in Ukrainian)

Strategy of JSC Ukrzaliznytsia for 2019-2023. Retrieved from https://www.uz.gov.ua/files/file/%D0%A1%D1%82%D1%80%D0%B0%D1%82%D0%B5%D0%B3%D1%96%D1%8F-4-Typography.pdf (in Ukraine)

Arp, R., Smith, B., & Spear, A. D. (2015). Building ontologies with basic formal ontology. Mit Press. DOI: https://doi.org/10.7551/mitpress/9780262527811.001.0001 (in English)

Asooja, K., Bordea, G., Vulcu, G., O'Brien, L., Espinoza, A., Abi-Lahoud, E., & Butler, T. (2015). Semantic annotation of finance regulatory text using multilabel classification. LeDA-SWAn, 8. Retrieved from http://cs.unibo.it/ledaswan2015/papers/asooja-et-al-ledaswan2015.pdf (in English)

Beden, S., Cao, Q., & Beckmann, A. (2021). SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0. Information, 12(8), 304-322. DOI: https://doi.org/10.3390/info12080304 (in English)

Benvenuti, F., Diamantini, C., Potena, D., & Storti, E. (2017). An ontology-based framework to support perfor-mance monitoring in public transport systems. Transportation Research Part C: Emerging Technologies, 81, 188-208. DOI: https://doi.org/10.1016/j.trc.2017.06.001 (in English)

Bischof, S., & Schenner, G. (2021, September). Rail Topology Ontology: A Rail Infrastructure Base Ontology. In The semantic web – ISWC 2021 (pp. 597-612). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-88361-4_35 (in English)

Caceres, P., Sierra-Alonso, A., Cuesta, C. E., & Vela, B. (2020). Improving urban mobility by defining a smart data integration platform. In IEEE Access (Vol. 8, pp. 204094-204113). DOI: https://doi.org/10.1109/access.2020.3033584 (in English)

Calvanese, D., Cogrel, B., Komla-Ebri, S., Lanti, D., Rezk, M., & Xiao, G. (2015). How to stay ontop of your data: Databases, ontologies and more. In The semantic web: ESWC 2015 satellite events (pp. 20-25). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-25639-9_4 (in English)

Calvanese, D., Gal, A., Haba, N., Lanti, D., Montali, M., Mosca, A., & Shraga, R. (2021, June). ADaMaP: Auto-matic Alignment of Relational Data Sources Using Mapping Patterns. In Advanced information systems engineering (pp. 193-209). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-79382-1_12 (in English)

Ceci, M., & Gangemi, A. (2016). An OWL ontology library representing judicial interpretations. Semantic Web, 7(3), 229-253. DOI: https://doi.org/10.3233/sw-140146 (in English)

Ceusters, W., & Smith, B. (2015). Aboutness: Towards foundations for the information artifact ontology. ICBO 2015, 47-51. (in English)

Chaves-Fraga, D., Pozo-Gilo, L., Toledo, J., Ruckhaus, E., & Corcho, O. (2020). Morph-CSV: Virtual Knowledge Graph Access for Tabular Data. Semantic Web, 12(6), 869-902. DOI: https://doi.org/10.3233/sw-210432 (in English)

Ciccarese, P., & Peroni, S. (2014). The Collections Ontology: creating and handling collections in OWL 2 DL frameworks. Semantic Web, 5(6), 515-529. DOI: https://doi.org/10.3233/sw-130121 (in English)

Corsar, D., Markovic, M., Edwards, P., & Nelson, J. D. (2015). The transport disruption ontology. In The seman-tic web – ISWC 2015 (pp. 329-336). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-25010-6_22 (in English)

Daconta, M. C., Obrst, L. J., & Smith, K. T. (2003). The Semantic Web: a guide to the future of XML, Web services, and knowledge management. Wiley. (in English)

De Meester, B., Seymoens, T., Dimou, A., & Verborgh, R. (2020). Implementation-independent function reuse. Future Generation Computer Systems, 110, 946-959. DOI: https://doi.org/10.1016/j.future.2019.10.006 (in English)

Debruyne, C., & McGlinn, K. (2021). Reusable SHACL Constraint Components for Validating Geospatial Linked Data. In Proceedings of the 4th International Workshop of Geospatial Linked Data (pp. 1-7). (in English)

Diamantini, C., Potena, D., & Storti, E. (2016). SemPI: A semantic framework for the collaborative construction and maintenance of a shared dictionary of performance indicators. Future Generation Computer Systems, 54, 352-365. DOI: https://doi.org/10.1016/j.future.2015.04.011 (in English)

Diamantopoulos, T., Roth, M., Symeonidis, A., & Klein, E. (2017). Software requirements as an application do-main for natural language processing. Language Resources and Evaluation, 51, 495-524. DOI: https://doi.org/10.1007/s10579-017-9381-z (in English)

Ding, L. Y., Zhong, B. T., Wu, S., & Luo, H. B. (2016). Construction risk knowledge management in BIM using ontology and semantic web technology. Safety science, 87, 202-213. DOI: https://doi.org/10.1016/j.ssci.2016.04.008 (in English)

Distinto, I., d'Aquin, M., & Motta, E. (2016). LOTED2: An ontology of European public procurement notices. Semantic Web, 7(3), 267-293. DOI: https://doi.org/10.3233/sw-140151 (in English)

Doerr, M., Gradmann, S., Hennicke, S., Isaac, A., Meghini, C., & Van de Sompel, H. (2010). The europeana data model. World Library and Information Congress: 76th IFLA general conference and assembly. Retrieved from https://www.ifla.org/past-wlic/2010/149-doerr-en.pdf (in English)

DOLCE: Descriptive Ontology for Linguistic and Cognitive Engineering. Retrieved from http://www.loa.istc.cnr.it/dolce/overview.html (in English)

Duarte, B. B., Falbo, R. A., Guizzardi, G., Guizzardi, R. S., & Souza, V. E. (2018). Towards an ontology of soft-ware defects, errors and failures. In Conceptual Modeling (pp. 349-362). Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-00847-5_25 (in English)

Dutta, B., & DeBellis, M. (2020). CODO: an ontology for collection and analysis of COVID-19 data. In Pro-ceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management-KEOD (pp. 76-85). Budapest, Hungary. DOI: https://doi.org/10.5220/0010112500760085 (in English)

EIM RINF. Retrieved from https://eimrail.org/document/rinf/ (in English)

Elizarov, A. M., Lipachev, E. K., Nevzorova, O. A., & Solov'ev, V. D. (2014). Methods and means for semantic structuring of electronic mathematical documents. Doklady Mathematics, 90, 521-524. DOI: https://doi.org/10.1134/s1064562414050275 (in English)

Enterprise Integration Laboratory-EIL. Retrieved from http://www.eil.utoronto.ca/theory/enterprise-modelling/tove/ (in English)

Fernández-López, M., Gomez-Pérez, A., & Juristo, N. (1997). Methontology: from ontological art towards ontological engineering. AAAI Technical Report SS-97-06, 24-26. (in English)

Fiorentini, X., Gambino, I., Liang, V. C., Rachuri, S., Mani, M., Nistir, C. B., & Turner, J. M. (2007). An ontology for assembly representation. Gaithersburg, MD : National Institute of Standards and Technology. DOI: https://doi.org/10.6028/nist.ir.7436 (in English)

Furini, F., Rai, R., Smith, B., Colombo, G., & Krovi, V. (2016). Development of a manufacturing ontology for functionally graded materials. In International Design Engineering Technical Conferences and Comput-ers and Information in Engineering Conference (pp. 1-11). Charlotte, North Carolina, USA. DOI: https://doi.org/10.1115/detc2016-59964 (in English)

Fürst, F., & Trichet, F. (2006). Heavy ontology engineering. In On the move to meaningful internet systems : OTM Confederate International Conferences (pp. 38-39). Berlin, Heidelberg. DOI: https://doi.org/10.1007/11915034_18 (in English)

Gangemi, A., Peroni, S., Shotton, D., & Vitali, F. (2017). The publishing workflow ontology (PWO). Semantic Web, 8(5), 703-718. DOI: https://doi.org/10.3233/sw-160230 (in English)

Ganino, G., Lembo, D., Mecella, M., & Scafoglieri, F. (2018). Ontology population for open-source intelligence: A GATE-based solution. Software: Practice and Experience, 48(12), 2302-2330. DOI: https://doi.org/10.1002/spe.2640 (in English)

Gayo, J. E. L., Prud'hommeaux, E., Solbrig, H. R., & Boneva, I. (2017). Validating and describing linked data portals using shapes. arXiv:1701.08924, 1-13. (in English)

Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5(2), 199-220. DOI: https://doi.org/10.1006/knac.1993.1008 (in English)

Guarino, N. (1997). Semantic matching: Formal ontological distinctions for information organization, extrac-tion, and integration. Information Extraction A Multidisciplinary Approach to an Emerging Information Technology (pp. 139-170). DOI: https://doi.org/10.1007/3-540-63438-x_8 (in English)

Guizzardi, G. (2005). Ontological foundations for structural conceptual models (PhD dissertation). Retrieved from https://ris.utwente.nl/ws/portalfiles/portal/6042428/thesis_Guizzardi.pdf (in English)

Guizzardi, G., de Almeida Falbo, R., & Guizzardi, R. S. (2008). Grounding software domain ontologies in the uni-fied foundational ontology. In Conference: Memorias de la XI Conferencia Iberoamericana de Software Engineering (CIbSE 2008) (pp. 127-140). Recife, Pernambuco, Brasil. (in English)

Hitzler, P., & Krisnadhi, A. (2018). Modular ontology modeling: a tutorial. Applications and practices in ontol-ogy design, extraction, and reasoning (pp. 3-20). DOI: https://doi.org/10.3233/ssw200032 (in English)

Hoekstra, R., Breuker, J., Di Bello, M., & Boer, A. (2007). The LKIF Core Ontology of Basic Legal Concepts. Loait, 321, 43-63. Retrieved from http://ceur-ws.org/Vol-321/LOAIT07-Proceedings.pdf#page=43 (in English)

Jakus, G., Milutinovic, V., Omerovic, S., & Tomazic, S. (2013). Concepts. Concepts, ontologies, and knowledge representations (pp. 5-27). DOI: https://doi.org/10.1007/978-1-4614-7822-5_4 (in English)

Jovic, A., Gamberger, D., & Krstacic, G. (2011). Heart failure ontology. Bio Algorithms Med Syst, 7(2), 101-110. (in English)

Julien, N. (2012). What We Know About Wikipedia: A Review of the Literature Analyzing the Project(s). SSRN electronic journal, 1-87. DOI: https://doi.org/10.2139/ssrn.2053597 (in English)

Katsumi, M., & Fox, M. (2018). Ontologies for transportation research: a survey. Transportation Research Part C: Emerging Technologies, 89, 53-82. DOI: https://doi.org/10.1016/j.trc.2018.01.023 (in English)

Katsumi, M., & Fox, M. (2020). City Transportation Planning Suite of Ontologies. Toronto: University of Toronto. (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)

Krima, S., Barbau, R., Fiorentini, X., Sudarsan, R., & Sriram, R. D. (2009). Ontostep: OWL-DL ontology for step. Gaithersburg, National Institute of Standards and Technology. DOI: https://doi.org/10.6028/nist.ir.7561 (in English)

Lališ, A., Bolčeková, S., & Štumbauer, O. (2020). Ontology-based reliability analysis of aircraft engine lubrication system. Transportation Research Procedia, 51, 37-45. DOI: https://doi.org/10.1016/j.trpro.2020.11.006 (in English)

Lewis, R. (2015). A semantic approach to railway data integration and decision support (PhD dissertation). University of Birmingham. (in English)

Malone, J., Brown, A., Lister, A. L., Ison, J., Hull, D., Parkinson, H., & Stevens, R. (2014). The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation. Journal of biomedical semantics, 5(25), 1-13. DOI: https://doi.org/10.1186/2041-1480-5-25 (in English)

Meehan, T. F., Masci, A. M., Abdulla, A., Cowell, L. G., Blake, J. A., Mungall, C. J., & Diehl, A. D. (2011). Logi-cal development of the cell ontology. BMC bioinformatics, 12(6), 1-12. DOI: https://doi.org/10.1186/1471-2105-12-6 (in English)

Mouromtsev, D. I., Shilin, I. A., Pliukhin, D. A., Baimuratov, I. R., & Rezeda, R. K. (2021). Building knowledge graphs of regulatory documentation based on semantic modeling and automatic term extraction. Scien-tific and Technical Journal of Information Technologies, Mechanics and Optics, 132(2), 256-266. DOI: https://doi.org/10.17586/2226-1494-2021-21-2-256-266 (in English)

Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012). Uberon, an integrative multi-species anatomy ontology. Genome biology, 13(R5), 1-20. DOI: https://doi.org/10.1186/gb-2012-13-1-r5 (in English)

Munnelly, G. (2020). Entity Linking for Text Based Digital Cultural Heritage Collections (PhD dissertation). Dublin. (in English)

Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford. Retrieved from https://protege.stanford.edu/publications/ontology_development/ontology101.pdf (in English)

Oberle, D., Grimm, S., & Staab, S. (2009). An ontology for software. In Handbook on ontologies: International Handbooks on Information Systems (INFOSYS) (pp. 383-402). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-540-92673-3_17 (in English)

O'connor, M. J., Halaschek-Wiener, C., & Musen, M. A. (2010). Mapping master: a flexible approach for mapping spreadsheets to OWL. In Lecture notes in computer science (pp. 194-208). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-17749-1_13 (in English)

Panov, P., Soldatova, L. N., & Džeroski, S. (2016). Generic ontology of datatypes. Information Sciences, 329, 900-920. DOI: https://doi.org/10.1016/j.ins.2015.08.006 (in English)

Pauwels, P., Van Deursen, D., Verstraeten, R., De Roo, J., De Meyer, R., Van de Walle, R., & Van Campenhout, J. (2011). A semantic rule checking environment for building performance checking. Automation in con-struction, 20(5), 506-518. DOI: https://doi.org/10.1016/j.autcon.2010.11.017 (in English)

Peroni, S. (2010). The Error Ontology Making constraints on ontology resources. Retrieved from https://sparontologies.github.io/error/current/error.html (in English)

Plu, J., & Scharffe, F. (2012). Publishing and linking transport data on the web. In WOD '12 : Proceedings of the First International Workshop (pp. 62-69). New York, USA. DOI: https://doi.org/10.1145/2422604.2422614 (in English)

Presutti, V., Daga, E., Gangemi, A., & Blomqvist, E. (2009, October). eXtreme design with content ontology design patterns. In WOP'09: Proceedings of the 2009 International Conference (pp. 83-97). Washington, USA. (in English)

Railway Domain Ontology. Retrieved from http://www.integrail.eu/documents/fs02.pdf (in English)

Rector, A., & Aranguren, M. E. (2003). Submissions: Normalization. Retrieved from http://ontologydesignpatterns.org/wiki/Submissions:Normalization (in English)

Roman, D., Alexiev, V., Paniagua, J., Elvesæter, B., von Zernichow, B. M., Soylu, A., & Taggart, C. (2022). The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information. Semantic Web, 13(1), 41-68. DOI: https://doi.org/10.3233/sw-210424 (in English)

Shah, N. H, Jonquet, C., Chiang, A. P., Butte, A. J., Chen, R., & Musen, M. A. (2009). Ontology-driven indexing of public datasets for translational bioinformatics. BMC bioinformatics 10(s1), 1-10. DOI: https://doi.org/10.1186/1471-2105-10-s2-s1 (in English)

Shynkarenko, V., & Zhuchyi, L. (2021). Ontological Harmonization of Railway Transport Information Systems. In COLINS-2021: 5th International Conference on Computational Linguistics and Intelligent Systems (Vol. 2870, pp. 541–554). Aachen, Germany. (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 (CSIT) (pp. 248-251). Lviv, Ukraine. DOI: https://doi.org/10.1109/CSIT52700.2021.9648761 (in English)

Skalozub, V., Ilman, V., & Shynkarenko, V. (2017). Development of ontological support of constructive-synthesizing modeling of information systems. Eastern-European Journal of Enterprise Technologies, 6(4(90)), 58-69. DOI: https://doi.org/10.15587/1729-4061.2017.119497 (in English)

Skalozub, V., Ilman, V., & Shynkarenko, V. (2018). Ontological support formation for constructive-synthesizing modeling of information systems development processes. Eastern-European Journal of Enterprise Technologies, 5(4(95)), 55-63. DOI: https://doi.org/10.15587/1729-4061.2018.143968 (in English)

Suárez-Figueroa, M. C., Gomez-Pérez, A., & Fernández-López, M. (2012). The neon methodology for ontology engineering. Ontology engineering in a networked world (pp. 9-34). Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-24794-1_2 (in English)

Szekely, P., Knoblock, C. A., Gupta, S., Taheriyan, M., & Wu, B. (2011). Exploiting semantics of web services for geospatial data fusion. The semantic web: semantics and big data (pp. 593-607). DOI: https://doi.org/10.1007/978-3-642-38288-8_40 (in English)

Szekely, P., Knoblock, C. A., Yang, F., Zhu, X., Fink, E. E., Allen, R., & Goodlander, G. (2013). Exploiting seman-tics of web services for geospatial data fusion. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Semantics and Ontologies-SSO '11 (pp. 32-39). New York, USA. DOI: https://doi.org/10.1145/2068976.2068981 (in English)

Tutcher, J. (2016). Development of semantic data models to support data interoperability in the rail industry (PhD dissertation). University of Birmingham. Retrieved from http://etheses.bham.ac.uk//id/eprint/6774/ (in English)

Verstichel, S., Ongenae, F., Loeve, L., Vermeulen, F., Dings, P., Dhoedt, B., & De Turck, F. (2011). Efficient data integration in the railway domain through an ontology-based methodology. Transportation Research Part C: Emerging Technologies, 19(4), 617-643. DOI: https://doi.org/10.1016/j.trc.2010.10.003 (in English)

Zhang, S., Boukamp, F., & Teizer, J. (2015). Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis (JHA). Automation in Construction, 52, 29-41. DOI: https://doi.org/10.1016/j.autcon.2015.02.005 (in English)

Zhao, L., Ichise, R., Mita, S., & Sasaki, Y. (2014). An ontology-based intelligent speed adaptation system for autonomous cars. Semantic technology (pp. 397-413). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-15615-6_30 (in English)

Zheng, J., Harris, M. R., Masci, A. M., Lin, Y., Hero, A., Smith, B., & He, Y. (2016). The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis. Journal of Biomedical Semantics, 7(53), 1-13. DOI: https://doi.org/10.1186/s13326-016-0100-2 (in English)