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Ontology-Based Semantic Checking of Data

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
291--319
Opis fizyczny
Bibliogr. 39 poz., rys., tab.
Twórcy
  • Ukrainian State University of Science and Technologies, 2 Lazaryana str., Dnipro, 49010, Ukraine
  • Ukrainian State University of Science and Technologies, 2 Lazaryana str., Dnipro, 49010, Ukraine
  • Ukrainian State University of Science and Technologies, 2 Lazaryana str., Dnipro, 49010, Ukraine
Bibliografia
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  • [3] Ceusters W., Smith B., Aboutness: Towards foundations for the information artifact ontology, in: Proceedings of the Sixth International Conference on Biomedical Ontology (ICBO), CEUR, 2015, 1-5.
  • [4] Chaves-Fraga D., Pozo-Gilo L., Toledo J., Ruckhaus E., Corcho Ó., Morph-CSV: Virtual Knowledge Graph Access for Tabular Data, in: ISWC (Demos/Industry), CEUR Workshop Proceedings, 2020, 11-16.
  • [5] Ciccarese P., Peroni S., The Collections Ontology: creating and handling collections in OWL 2 DL frameworks, Semantic Web, 5, 7, 2014, 515-529.
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  • [8] Diamantini C., Potena D., Storti E. Sem PI, A Semantic Framework for the Collaborative Construction and Maintenance of a Shared Dictionary of Performance Indicators, Future Generation Computer Systems (FGCS), 54, 2016, 352-365.
  • [9] Falco R., Gangemi A., Peroni S., Shotton D., Vitali F., Modeling OWL ontologies with Graffoo, in: European Semantic Web Conference, Springer, Cham, 2014, 320-325.
  • [10] Gangemi A., Peroni, S., Shotton D., Vitali F., The publishing workflow ontology (PWO), Semantic Web, 8, 5, 2017, 703-718.
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  • [12] Gayo JEL, Prud'hommeaux E., Solbrig HR, Boneva I., Validating and describing linked data portals using shapes, CoRR, abs/1701.08924, 2017.
  • [13] Giunchiglia F., Zaihrayeu I., Lightweight Ontologies, in: Liu L., Özsu MT (eds.), Encyclopedia of Database Systems, Springer, Boston, 2009. https://doi.org/10.1007/978-0-387 -39940-9_1314
  • [14] Gómez-Pérez A., From knowledge based systems to knowledge sharing technology: Evaluation and assessment, Stanford University, 1994.
  • [15] Guarino N., Giaretta P. Ontologies and knowledge bases, in: Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, 1995, 25-32.
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  • [19] Kozachenko D., Berezovyi N., Malashkin V., Arbuzov M., Skovron I., Development of the typical passport structure of industrial railways, Transport systems and transportation technologies, 14, 2017, 42-49.
  • [20] Lewis R., A semantic approach to railway data integration and decision support, University of Birmingham, 2015.
  • [21] Malone J., Brown A., Lister AL, Ison J., Hull D., Parkinson H., Stevens R., The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation, Journal of biomedical semantics, 5, 1, 2014, 1-13.
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  • [23] Noy N.F., McGuinness D.L., Ontology Development 101: A Guide to Creating Your First Ontology'', Stanford Knowledge Systems Laboratory and Stanford Medical Informatics, 2001.
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  • [25] Panov P., Džeroski S., Soldatova L.N., Representing entities in the OntoDM data ontology, in: Inductive mining Databases and Constraint-Based Data Mining, Springer, New York, 2010, 27-58.
  • [26] Panov P., Soldatova L.N., Džeroski S., Generic ontology of datatypes, Information Sciences, 329, 2016, 900-920.
  • [27] Pauwels P., Van De ursen D., Verstraeten R., De Roo J., De Meyer R., Van de Walle R., Van Campenhout J., A semantic rule checking environment for building performance checking, Automation in construction, 20, 5, 2011, 506-518
  • [28] Peroni S., Error Ontology https://github.com/SPAROntologies/error. [Accessed November 19, 2021].
  • [29] Roman D., Alexiev V., Paniagua J., Elvesæter B., von Zernichow BM, Soylu A., Simeonov B., Taggart C., The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information, Semantic Web, 13, 1, 2022, 41-68.
  • [30] Rosen G., Analysis of Tabula: A PDF-Table extraction tool, Uppsala University, Uppsala, 2019.
  • [31] Shynkarenko V., Zhuchyi L., Ontological Harmonization of Railway Transport Information Systems, International Conference on Computational Linguistics and Intelligent Systems, CEUR-WS Team, Aachen, Germany, 2021, 541–554.
  • [32] Shynkarenko V., Zhuchyi L., Ivanov O., Conceptualization of the tabular knowledge representation, International Conference on Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, 2021.
  • [33] Skalozub V., Ilman V., Shynkarenko V., Development of ontological support of constructive synthesizing modeling of information systems, Eastern-European Journal of Enterprise Technologies, 6, 4, 2017, 58-69.
  • [34] Skalozub V., Ilman V., Shynkarenko V., Ontological support formation for constructive-synthesizing modeling of information systems development processes, Eastern-European Journal of Enterprise Technologies, 5, 4(95), 2018, 55-63.
  • [35] Tutcher J., Development of semantic data models to support data interoperability in the rail industry, University of Birmingham, 2016.
  • [36] Zhang S., Boukamp F., Teizer J., Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis (JHA), Automation in Construction, 52, 2015, 29-41.
  • [37] Directive 2008/57/E C of the European Parliament and of the Council of 17 June 2008 on the interoperability of the rail system within the Community.
  • [38] EZhDA Contact Group. Analysis of parameters defining to maintain the technical and operational compatibility of the 1520 mm track railway system at the cis-EU border. subsystem:infrastructure. Railroad and travel facilities.
  • [39] Relation Ontology https://obofoundry.org/ontology/ro.html. [Accessed 12 April 2022].
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ff562a46-d48e-4033-9836-035d0750ab1f
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