Abstract
Large-scale Internet-of-Things (IoT) environments as being found in critical infrastructures such as Intelligent Transportation Systems (ITS) are characterized by (i) massive heterogeneity of data, (ii) prevalent legacy systems, and (iii) continuous evolution of operational technology. In such environments, the realization of crosscutting services demands a conceptual IoT representation, most promising, in terms of a domain ontology. Populating the ontology’s A-Box, however, faces some challenges, which are not sufficiently addressed by now. In this respect, the contribution of this short paper is three-fold: Firstly, in order to point out the complexity of addressed real-world IoT environments, we identify prevalent challenges for (semi-)automatic ontology population by means of a real world example. Secondly, in order to address these challenges, we elaborate on related work by identifying promising lines of research relevant for ontology population. Thirdly, based thereupon, we sketch out a solution approach towards message-driven ontology population.
This work is supported by the Austrian Research Promotion Agency (FFG) under grant FFG Forschungspartnerschaften 874490.
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Based on the domain specific object type catalog defined by the operating company.
References
Appice, A.: Towards mining the organizational structure of a dynamic event scenario. J. Intell. Inf. Syst. 50(1), 165–193 (2018)
Augusto, A., et al.: Automated discovery of process models from event logs: review and benchmark. IEEE Trans. Knowl. Data Eng. 31(4), 686–705 (2018)
Belkaroui, R. et al.: Towards events ontology based on data sensors network for viticulture domain. In: Proceedings of the International Conference on the Internet of Things, pp. 1–7. ACM (2018)
Detro, S. et al.: Enhancing semantic interoperability in healthcare using semantic process mining. In: Proceedings of the International Conference on Information Society and Technology, pp. 80–85 (2016)
Endler, M. et al.: Towards stream-based reasoning and machine learning for IoT applications. In: Intelligent System Conference, pp. 202–209. IEEE (2017)
Ganino, G., et al.: Ontology population for open-source intelligence: a GATE-based solution. Softw. Pract. Exp. 48(12), 2302–2330 (2018)
Graf, D., Kapsammer E., Schwinger W., Retschitzegger W., Baumgartner N.: Cutting a path through the IoT ontology jungle - a meta survey. In: International Conference on Internet of Things and Intelligence Systems. IEEE (2019)
Graf, D., Retschitzegger W., Schwinger W., Kapsammer E., Baumgartner N., Pröll B.: Towards operational technology monitoring in intelligent transportation systems. In: International Conference on Management of Digital Eco-Systems. ACM (2019)
Jafari, M., et al.: Role mining in access history logs. J. Comput. Inf. Syst. Ind. Manag. Appl. 1, 258–265 (2009)
Jayawardana, V. et al.: Semi-Supervised instance population of an ontology using word vector embeddings. In: Proceedings of the International Conference on Advances in ICT for Emerging Regions, pp. 217–223. IEEE (2017)
Jin, T., et al.: Organizational modeling from event logs. In: Proceedings of the International Conference on Grid and Cooperative Computing, pp. 670–675. IEEE (2007)
Lin, S., et al.: Dynamic data driven-based automatic clustering and semantic annotation for internet of things sensor data. Sens. Mater. 31(6), 1789–1801 (2019)
Liu, F., et al.: Device-oriented automatic semantic annotation in IoT. J. Sens. 2017, 9589064:1–9589064:14 (2017)
Lubani, M., et al.: Ontology population: approaches and design aspects. J. Inf. Sci. 45(4), 502–515 (2019)
Matzner, M., Scholta, H.: Process mining approaches to detect organizational properties in CPS. In: European Conference on Information Systems (2014)
Ni, Z., et al.: Mining organizational structure from workflow logs. In: Proceedings of the International Conference on e-Education, Entertainment and e-Management, pp. 222–225. IEEE (2011)
Reyes-Ortiz, J., et al.: Web services ontology population through text classification. In: Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 491–495. IEEE (2016)
Van Der Aalst, W., et al.: Process mining manifesto. In: Proceedings of the International Conference on Business Process Management, pp. 169–194. Springer (2011)
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Graf, D., Schwinger, W., Kapsammer, E., Retschitzegger, W., Pröll, B., Baumgartner, N. (2020). Towards Message-Driven Ontology Population - Facing Challenges in Real-World IoT. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_37
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