An Ontology – based Contextual Approach for Cross-domain Applications in Internet of Things

Sihem Benkhaled, Mounir Hemam, Meriem Djezzar, Moufida Maimour


The Internet of Things is an ecosystem which enables objects and devices such as sensors or actuators to communicate and exchange information with each other without human intervention. One of the main challenges in the Internet of Things is the lack of semantic interoperability; devices cannot understand the meaning of raw data, due to the diversity and heterogeneity in data formats from different sources. In order to deal with semantic interoperability, the ontologies are the one way to integrate semantics to raw data; they describe an IoT system and represent the data in a standardized way. The IoT devices provide a great deal of IoT data, mainly used for specific IoT applications such as smart home, smart farming, smart cities or healthcare. Therefore, existing applications became isolated in vertical silos, each one of them use independently their own model (i.e. ontology), which makes this ontologies also limited to a specific domain. Our approach has the goal of breaking down these vertical silos and achieves a semantic interoperability across IoT domains in cross-domain applications. In this paper, we have proposed a development of a single cross-domain ontology named CDOnto, it is considered to be a generic across different IoT domains, which can be extended by domain-specific ontologies. The proposed model follows a contextual approach to organize and distinguish the combined domains (i.e. contexts) representations. In addition, the ontology allows reasoning across overlapping domains and infers a complementary and new knowledge required in cross-domain applications.

Full Text:



Almeida, A., Lo´pez-de Ipin˜a, D., (2012). Assessing ambiguity of context data in intelligent environments: Towards a more reliable context managing system. Sensors 12, 4934–4951.

Ardjani, F., (2015). Ontology-alignment techniques: Survey and analysis. International Journal of Modern Education and Computer Science 11, 67–78.

Bajaj, G., Agarwal, R., Singh, P., Georgantas, N., Issarny, V., (2017). A study of existing ontologies in the iot-domain. arXiv preprint arXiv:1707.00112 .

Benslimane, D., Arara, A., Falquet, G., Maamar, Z., Thiran, P., Gargouri, F., (2006). Contextual ontologies, in: International Conference on Advances in Information Systems, Springer. pp. 168–176.

Borgida, A., Serafini, L., (2003). Distributed description logics: Assimilating information from peer sources, in: Journal on data semantics I. Springer, pp. 153–184.

Bouquet, P., Giunchiglia, F., Van Harmelen, F., Serafini, L., Stuckenschmidt, H., (2003). C-owl: Contextualizing ontologies, in: International Semantic Web Conference, Springer. pp. 164–179.

Bouquet, P., Giunchiglia, F., Van Harmelen, F., Serafini, L., Stuckenschmidt, H., (2004). Contextualizing ontologies. Journal of Web Semantics 1, 325–343.

Daniele, L., den Hartog, F., Roes, J., (2015). Created in close interaction with the industry: the smart appliances reference (saref) ontology, in: International Workshop Formal Ontologies Meet Industries, Springer. pp. 100–112.

Daniele, L., Solanki, M., den Hartog, F., Roes, J., (2016). Interoperability for smart appliances in the iot world, in: International Semantic Web Conference, Springer. pp. 21–29.

Dey, S., Dasgupta, R., (2014). Sensor knowledge representation with spatiotemporal annotation: An energy sensor ontology use case, in: IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), IEEE. pp. 455–459.

Gyrard, A., (2013). A machine-to-machine architecture to merge semantic sensor measurements, in: Proceedings of the 22nd International Conference on World Wide Web, pp. 371–376.

Hemam, M., (2018). An extension of the ontology web language with multi-viewpoints and probabilistic reasoning. International Journal of Advanced Intelligence Paradigms 10, 247–265.

Huang, X., Yi, J., Zhu, X., Chen, S., (2016). A semantic approach with decision support for safety service in smart home management. Sensors 16. URL:

Lee, K., Lee, J., Kwan, M.P., (2017). Location-based service using ontology-based semantic queries: A study with a focus on indoor activities in a university context. Computers, Environment and Urban Systems 62, 41–52.

Mishra, S., Jain, S., (2020). Ontologies as a semantic model in iot. International Journal of Computers and Applications 42, 233–243.

Okeyo, G., Chen, L., Wang, H., Sterritt, R., (2014). Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive and Mobile Computing 10, 155–172.

Ozaki, A., (2020). Learning description logic ontologies: Five approaches. Where do they stand ? KI-Ku¨nstliche Intelligenz 34, 317–327.

Seydoux, N., Drira, K., Hernandez, N., Monteil, T., (2016). Iot-o, a core-domain iot ontology to represent connected devices networks, in: European Knowledge Acquisition Workshop, Springer. pp. 561–576.

Sudhana, K.M., Raj, V.C., Suresh, R., (2013). An ontology-based framework for context-aware adaptive e-learning system, in: International Conference on Computer Communication and Informatics, IEEE. pp. 1–6.

Venkatesh, J., Chan, C., Akyurek, A.S., Rosing, T.S., (2016). A modular approach to context-aware iot applications, in: IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI), IEEE. pp. 235–240.

Wang, X., Ji, Q., (2012). Learning dynamic bayesian network discriminatively for human activity recognition, in: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), IEEE. pp. 3553–3556.

Woznowski, P.R., Tonkin, E.L., Flach, P.A., (2018). Activities of daily living ontology for ubiquitous systems: Development and evaluation. Sensors 18, 2361.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.