Abstract
Handling large-scale heterogeneous data in the IoT and processing it in real time will be a key factor towards realizing IoT services in users’ daily lives. In this regard, semantic domain ontologies are increasingly seen as a solution for enabling interoperability across heterogeneous data and sensor-based applications. Several ontologies have been proposed with the aim of addressing interoperability issues and various aspects of IoT device observation. However, for most of these ontologies, they are either in the domain of sensor networks or the much broader domain of the IoT. Furthermore, these ontologies have shown slow improvement, as they have been developed by limited groups of domain experts. This paper proposes a model that exploits the collective intelligence which emerges from social tagging systems to generate up-to-date domain-specific ontologies. The evaluation of the proposed model, using a dataset extracted from BibSonomy, demonstrated that the model could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed model introduces a simple and effective method for the common problems related to tag ambiguity.
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Alruqimi, M., Aknin, N., Al-Hadhrami, T., James-Taylor, A. (2019). Towards Semantic Interoperability for IoT: Combining Social Tagging Data and Wikipedia to Generate a Domain-Specific Ontology. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_34
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