Impact of Ontology in Aviation Incident and Accident Knowledge Repository
Abstract
The aviation industry is one of the most critical industries where access to quick and detailed information is very crucial for saving human lives and preserving the life span of the expensive fleet of aircraft. One of the most important problems with accident and incident reports in the aviation industry is that they are usually manually based or accessed and, in most cases, the outcome is usually based on hardcopy reports or standalone softcopies and are fragmented on the World Wide Web, and this makes logical inference cumbersome. However, documenting accidents and incidents in a database system is a good way to make these reports easily and logically accessible as knowledge for making informed decisions on any potential aspects to take note of, in order to enhance aircraft safety. This paper presents an Ontology-based knowledge representation language to facilitate explicit interpretation of domain resources and data. Coupled with its interoperability function, ontology presents the best choice for modelling such database domain. Ontology was developed in Protégé using the template of the accident and incident manuals and was tested by querying it for accident and incident information which returned results based on the search criteria and the information in its database. Access to logical information in a database system for accidents and incidents in the aviation industry would facilitate the understanding of the structure of the data that can be used by humans as a source of knowledge of the things in the domain of a function for software for further computational task or analysis would go a long way in mitigating some avoidable incidences.
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Aghdam M.Y, Tabbakh S.R.K., Chabok S.J.M. & Kheyrabadi M. (2021). Ontology generation for flight safety messages in air traffic management. J Big Data, 8:61
Asim M.N., Wasim, M., Khan M.U.G, Mahmood W., & Abbasi H.M. (2018). A survey of ontology learning techniques and applications, Database, pp. 1–24
Chinedu, P. U., Nwankwo, W., Aliu, D., Shaba, S. M., & Momoh, M. O. Cloud Security Concerns: Assessing the Fears of Service Adoption
D. Aliu, S. M. Shaba, M. O. Momoh, P. U Chinedu and W. Nwankwo “A Computer Security System for Cloud Computing Based on Encryption Technique” Computer Engineering and Applications 10(1), February 2021,. (pp. 41-54).
Haendel M. A., Christopher G., Chute M.D., & Robinson P. N. (2018). Classification, Ontology, and Precision Medicine, N Engl J Med, 379, pp. 1452-1462.
Hughes P., Robinson R., Figueres-Esteban M., & Gulijk C.V. (2019). Extracting Safety Information from Multi-lingual accident reports using an Ontology-based Approach, Safety Science, 118, pp. 288-297.
Kondylakis H., Nikolaos A., Dimitra P., Anastasios K., Emmanouel K., Kyriakos K., Iraklis S., Stylianos K., & Papadakis N. (2021). Delta: A Modular Ontology Evaluation System. Information, 12, 301
Kremen P., Kostov B., Ahmed M.B.J., Plos V., Lalis A., Stojic S., & Vittek P. (2017). Ontological Foundations of European Coordination Center for Accident and Incident Reporting Systems, Journal of Aerospace Information Systems, 14(5), pp. 279-292.
Ledvinka M., Lalis A., & Kremen P. (2019). Towards Data-driven Safety: An Ontology-based information System. Journal of Aerospace Information Systems, 16(1), pp. 22-36
Milambo D., & Phiri J. (2019). Aircraft Spares Supply Chain Management for the Aviation Industry in Zambia Based on the Supply Chain Operations Reference (SCOR) Model. Open Journal of Business and Management, 7, pp. 1183-1195
Otuka R.I., Tawil A.-R. & Al-Nemrat, A. (2017). Cloudysme: An Ontological Framework for Aiding SMEs Adoption of SaaS in a Cloud Environment. Journal of Computer and Communications, 5, pp. 86-112.
Procedures Manual of Aircraft Accident/Incident Investigation. Accessed on 15th June, 2021 https://www.civilaviation.gov.in/sites/default/files/Current%20Procedure%20Manual%20for%20Accident%20Investigation.pdf
Saeeda L. (2017). Iterative Approach for Information Extraction and Ontology Learning from Textual Aviation Safety Reports. European Semantic Web Conference, pp. 236-245
Shobowale K.O. (2018). Ontology Knowledge Based System Tool for Technology Decision Support for Subsea Multiphase Pump, Unpublished PhD Thesis, Universiti Teknologi PETRONAS
Su K-W, Yang C-H & Huang P-H (2016). The Construction of an Ontology-Based Knowledge Management Model for Departure Procedures. Advances in Aerospace Science and Technology, 1 ,pp. 32-47
Talib A.M., Alomary F.O., Alwadi H.F., & Albusayli, R.R. (2018). Ontology-Based Cyber Security Policy Implementation in Saudi Arabia. Journal of Information Security, 9, pp. 315-333.
Yang Z. & Qing L. (2011). Ontology Modelling and Semantic Retrieval for Aircraft Fault Knowledge, Computer Engineering and Applications, 16.
Zhao Q., Li Q., & Wen J. (2018). Construction and application research of knowledge graph in aviation risk field, MATEC Web of Conferences 151, 05003
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