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Construction of a base ontology to represent accident expertise knowledge

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Abstract

Expertise is an activity carried out by experts that contributes to societal progress, as it helps to elucidate unknown situations. For example, accident expertise eases accident understanding by describing how it happened and by identifying its causes and consequences. As a result, the design of accident expertise in a convenient human–machine structure will enable the querying, reasoning, and reuse of accident knowledge in other tools, such as safety and decision-making systems. However, existing representations of accident knowledge, such as documents, relational databases, or accident ontologies, do not fulfill accident expertise expectations. Moreover, these representations are unlikely to provide the appropriate use of accident expertise knowledge. This study presents a base ontology for accident expertise knowledge representation designed with a model-driven methodology and implemented with semantic web technologies. The study obtained satisfactory results from the evaluation and application of extension and reuse of this ontology with aircraft accident expertise taken from the French bureau of Enquiries and Analysis (BEA) for civil aviation safety.

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Notes

  1. http://protege.stanford.edu/.

  2. https://git.enit.fr/ssonfack/baeo.

  3. https://www.w3.org/2012/06/rao.html.

References

  • Amirhosseini M, Salim J (2019) A synthesis survey of ontology evaluation tools, applications and methods to propose a novel branch in evaluating the structure of ontologies: graph-independent approach. Int J Comput 33(1):46–68

    Google Scholar 

  • Barrachina J, Garrido P, Fogue M, Martinez FJ, Cano J-C, Calafate CT, Manzoni P (2012) Veacon: a vehicular accident ontology designed to improve safety on the roads. J Netw Comput Appl 35(6):1891–1900

    Google Scholar 

  • Barrachina J, Garrido P, Fogue M, Martinez F J, Cano J -C, Calafate C T, Manzoni P (2012) Caova: a car accident ontology for vanets. In: 2012 IEEE wireless communications and networking conference (WCNC), pp. 1864–1869, Ieee

  • Baskara S, Yaacob H, Hainin M, Hassan S, Mashros N, Yunus N, Hassan N, Warid M, Idham M, Ismail C et al (2019) Influence of pavement condition towards accident number on Malaysian highway. IOP Conf Ser Earth Environ Sci 220:012008

    Google Scholar 

  • Bock J, Haase P, Ji Q, Volz R (2008) Benchmarking owl reasoners. In: ARea2008-Workshop on Advancing Reasoning on the Web: Scalability and Commonsense, Tenerife Spain

  • Brank J, Grobelnik M, Mladenic D (2005) “A survey of ontology evaluation techniques,” in Proceedings of the conference on data mining and data warehouses (SiKDD 2005), pp. 166–170, Citeseer Ljubljana Slovenia

  • Bravo G, Castellucci H, Lavallière M, Arezes P, Martínez M, Duarte G (2022) The influence of age on fatal work accidents and lost days in Chile between 2015 and 2019. Saf Sci 147:105599

    Google Scholar 

  • Cabrera O, Franch X, Marco J (2019) 3lconont: a three-level ontology for context modelling in context-aware computing. Softw Syst Model 18(2):1345–1378

    Google Scholar 

  • Chuvikov D, Varlamov O, Aladin D, Chernenkiy V, Baldin A (2019) Mivar models of reconstruction and expertise of emergency events of road accidents. IOP Conf Ser Mater Sci Eng 534:012007

    Google Scholar 

  • Cimmino A, Fernández-Izquierdo A, García-Castro R (2020) Astrea: automatic generation of shacl shapes from ontologies. In: European Semantic Web Conference, Springer, pp. 497–513

  • Das S, Hussey P (2021) Contsonto: a formal ontology for continuity of care. In: pHealth 2021 , IOS Press, pp. 82–87

  • de Araújo SE, Valentin E, Carvalho JRH, da Silva BR (2021) A survey of model driven engineering in robotics. J Comput Lang 62:101021

    Google Scholar 

  • De Lope RP, Medina-Medina N, Urbieta M, Lliteras AB, García AM (2021) A novel uml-based methodology for modeling adventure-based educational games. Entertain Comput 38:100429

    Google Scholar 

  • De Nicola A, Missikoff M, Navigli R (2009) A software engineering approach to ontology building. Inf Syst 34(2):258–275

    Google Scholar 

  • Djurić D, Gašević D, Devedžić V, Damjanović V (2004) A uml profile for owl ontologies. Model driven architecture. Springer, pp 204–219

    Google Scholar 

  • Farrington-Darby T, Wilson JR (2006) The nature of expertise: a review. Appl Ergon 37(1):17–32

    Google Scholar 

  • Fionda V, Pirrò G (2019) Ontology: definition languages

  • Gašević D, Djurić D, Devedžić V (2006) Model driven architecture and ontology development. Springer

    Google Scholar 

  • Gaševic D, Djuric D, Devedžic V (2009) Model driven engineering and ontology development. Springer Science & Business Media, Berlin

    Google Scholar 

  • Gayo JEL, Prud’Hommeaux E, Boneva I, Kontokostas D (2017) Validating rdf data. Synth Lect Seman Web: Theory Technol 7(1):1–328

    Google Scholar 

  • Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220

    Google Scholar 

  • Guergour H-E, Driouche R, Boufaïda Z (2006) An approach for application ontology building and integration enactment. In: SWAP

  • Guermah H, Fissaa T, Hafiddi H, Nassar M, Kriouile A (2014) An ontology oriented architecture for context aware services adaptation. arXiv preprint. http://arxiv.org/abs/1404.3280

  • Guizzardi G, Botti Benevides A, Fonseca CM, Porello D, Almeida JPA, Prince Sales T (2021) Ufo: unified foundational ontology. Appl Ontol 17:1–44

    Google Scholar 

  • Hassan MM, Mokhtar HM (2021) Autismont: an ontology-driven decision support for autism diagnosis and treatment. Egypt Inform J 23:95–103

    Google Scholar 

  • Horrocks I (2005) Owl: A description logic based ontology language. In: International conference on principles and practice of constraint programming. Springer, pp. 5–8

  • Hur A, Janjua N, Ahmed M (2021) A survey on state-of-the-art techniques for knowledge graphs construction and challenges ahead. arXiv preprint http://arxiv.org/abs/2110.08012

  • Jain S (2021) Understanding semantics-based decision support. CRC Press

    Google Scholar 

  • Jean S, Pierra G, Ait-Ameur Y (2007) Domain ontologies: a database-oriented analysis. Web information systems and technologies. Springer, pp 238–254

    Google Scholar 

  • Jetlund K, Onstein E, Huang L (2019) Adapted rules for uml modelling of geospatial information for model-driven implementation as owl ontologies. ISPRS Int J Geo Inform 8(9):365

    Google Scholar 

  • Kaindl H, Rathfux T, Hulin B, Beckert R, Arnautovic E, Popp R (2016) A core ontology of safety risk concepts. Human-centered and error-resilient systems development. Springer, pp 165–180

    Google Scholar 

  • Karhu K (2002) Expertise cycle-an advanced method for sharing expertise. J Intell Cap 3:430–446

    Google Scholar 

  • Kogut P, Cranefield S, Hart L, Dutra M, Baclawski K, Kokar M, Smith J (2002) Uml for ontology development. Knowl Eng Rev 17(1):61–64

    Google Scholar 

  • Křemen P, Kostov B, Blaško M, Ahmad J, Plos V, Lališ A, Stojić S, Vittek P (2017) Ontological foundations of European coordination centre for accident and incident reporting systems. J Aerosp Inform Syst 14(5):279–292

    Google Scholar 

  • Maalel A, Mejri L, Mabrouk H H, Ghezela H B (2012) Towards an ontology of help to the modeling of accident scenarii: Application on railroad transport. In: 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 1–6, IEEE

  • Malgouyres H, Motet G (2006) A uml model consistency verification approach based on meta-modeling formalization. In: Proceedings of the 2006 ACM symposium on Applied computing, pp. 1804–1809

  • Martínez-Costa C, Schulz S (2017) Validating ehr clinical models using ontology patterns. J Biomed Inform 76:124–137

    Google Scholar 

  • Martínez-García JR, Castillo-Barrera F-E, Palacio RR, Borrego G, Cuevas-Tello JC (2020) Ontology for knowledge condensation to support expertise location in the code phase during software development process. IET Softw 14(3):234–241

    Google Scholar 

  • Mascardi V, Cordì V, Rosso P (2007) A comparison of upper ontologies. In: Woa, vol. 2007, Citeseer, pp. 55–64

  • MOF O (2015) Omg meta object facility (mof) core specification. Version 2.4. 2. April 2014

  • Musen M A, Team T P (2013) Protégé ontology. Springer New York, New York, pp 1763–1765

    Google Scholar 

  • Naubourg P, Savonnet M, Leclercq É, Yétongnon K (2011) A approach to clinical proteomics data quality control and import. In: International Conference on Information Technology in Bio-and Medical Informatics. Springer, pp. 168–182

  • Navarro C, Colbach N (2020) Méthodes d’expertise-comment les utiliser?

  • Nowobilski T, Hoła B (2023) Methodology based on causes of accidents for forcasting the effects of falls from scaffoldings using the construction industry in poland as an example. Saf Sci 157:105945

    Google Scholar 

  • Noy N F, McGuinness D L et al. (2001) Ontology development 101: A guide to creating your first ontology

  • Nwagu CK, Omankwu OC, Inyiama H (2017) Knowledge discovery in databases (kdd): an overview. Int J Comput Sci Inf Secur (IJCSIS) 15(12):13–16

    Google Scholar 

  • Odm O (2007) Ontology definition metamodel: Omg adopted specification. Object Manag Group 26(05):2008

    Google Scholar 

  • Özacar T (2022) Extending ontology pitfalls for better ontology evaluation. J Inform Sci, 01655515221110990

  • Panagiotopoulos I, Kalou A, Pierrakeas C, Kameas A (2012) An ontological approach for domain knowledge modeling and management in e-learning systems. In: IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer, pp. 95–104

  • Paolone G, Marinelli M, Paesani R, Di Felice P (2020) Automatic code generation of mvc web applications. Computers 9(3):56

    Google Scholar 

  • Pareti P, Konstantinidis G (2021) A review of shacl: From data validation to schema reasoning for rdf graphs. arXiv preprint http://arxiv.org/abs/2112.01441

  • Poveda-Villalón M, Suárez-Figueroa M C, Gómez-Pérez A (2012) Validating ontologies with oops!. In: Knowledge Engineering and Knowledge Management: 18th International Conference, EKAW 2012, Galway City, Ireland, October 8–12, 2012. Proceedings 18, pp. 267–281, Springer

  • Poveda-Villalón M, Gómez-Pérez A, Suárez-Figueroa MC (2014) OOPS! (OntOlogy Pitfall Scanner!): an on-line tool for ontology evaluation. Int J Seman Web Inform Syst (IJSWIS) 10(2):7–34

    Google Scholar 

  • Rafindadi AD, Napiah M, Othman I, Mikić M, Haruna A, Alarifi H, Al-Ashmori YY (2022) Analysis of the causes and preventive measures of fatal fall-related accidents in the construction industry. Ain Shams Eng J 13(4):101712

    Google Scholar 

  • Roventa E, Spircu T (2009) Management of knowledge imperfection in building intelligent systems. Springer

    MATH  Google Scholar 

  • Sene A, Kamsu-Foguem B, Rumeau P (2018) Decision support system for in-flight emergency events. Cogn Technol Work 20:245–266

    Google Scholar 

  • Skalle P, Aamodt A, Laumann K (2014) Integrating human related errors with technical errors to determine causes behind offshore accidents. Saf Sci 63:179–190

    Google Scholar 

  • Uschold M, King M (1995) Towards a methodology for building ontologies. Citeseer

  • Uschold M, Gruninger M (1996) Ontologies: principles, methods and applications. knowl Eng Rev 11(2):93–136

    Google Scholar 

  • Vanderhaegen F (2021) Heuristic-based method for conflict discovery of shared control between humans and autonomous systems-a driving automation case study. Robot Auton Syst 146:103867

    Google Scholar 

  • Vo MHL, Hoang Q (2020) Transformation of uml class diagram into owl ontology. J Inform Telecommun 4(1):1–16

    Google Scholar 

  • Wang J, Wang X (2011) An ontology-based traffic accident risk mapping framework. In: International Symposium on Spatial and Temporal Databases. Springer, pp 21–38

  • Wieten S (2018) Expertise in evidence-based medicine: a tale of three models. Philos Ethics Humanit Med 13(1):1–7

    Google Scholar 

  • Wu H, Zhong B, Medjdoub B, Xing X, Jiao L (2020) An ontological metro accident case retrieval using CBR and NLP. Appl Sci 10(15):5298

    Google Scholar 

  • Zhong B, Ding L, Love PE, Luo H (2015) An ontological approach for technical plan definition and verification in construction. Autom Constr 55:47–57

    Google Scholar 

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Authors and Affiliations

  1. Production Engineering Laboratory/UFTMiP 47, Avenue d’Azereix, BP 1629, 65016, Tarbes Cedex, France

    Serge Sonfack Sounchio, Bernard Kamsu-Foguem & Laurent Geneste

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  1. Serge Sonfack Sounchio
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  2. Bernard Kamsu-Foguem
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Contributions

SSS conceptualization, methodology, formal analysis, writing—original draft. BKF supervision, writing - review & editing, resources, validation. LG supervision, writing - review & editing, resources, validation.

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Correspondence to Serge Sonfack Sounchio.

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Sonfack Sounchio, S., Kamsu-Foguem, B. & Geneste, L. Construction of a base ontology to represent accident expertise knowledge. Cogn Tech Work 25, 183–201 (2023). https://doi.org/10.1007/s10111-023-00724-8

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