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Context-Aware Computing for CPS
Published in G.R. Karpagam, B. Vinoth Kumar, J. Uma Maheswari, Xiao-Zhi Gao, Smart Cyber Physical Systems, 2020
Bhuvaneswari Arunagiri, Maheswari Subburaj
The semantic sensor ontology is the ontology that describes the connected sensors and the data collected by them, their features and also their actuators. SSN (Semantic Sensor Network) follows modularized architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties, as shown in Figure 11.5. Ontology modularization is used to segment ontology into smaller parts. SSN Ontology integrates and maps the available ontologies to create a new ontology. SSN Ontology applies both vertical and horizontal modularization, where vertical means the unidirectional import of one ontology from another, and horizontal means the bidirectional import between any two ontologies. For example, Dolce Ontology imports from SSN Ontology (SSNO) but the reverse does not happen, and hence it is vertical segmentation. The sample relations module depends on SSNO and the reverse also happens, so this is horizontal segmentation.
Semantic driven code generation for networking testbed experimentation
Published in Enterprise Information Systems, 2018
Filip Jelenkovic, Milorad Tosic, Valentina Nejkovic
The proposed SecGENE ontology framework follows best practices in ontology modularization (Stuckenschmidt et al., 2009) by identifying components (modules) that can be considered separately while they are interlinked with other modules. In this way, it facilitates encapsulation and reuse of knowledge. As a consequence, arbitrary domain ontologies can be used within the SecGENE framework in order to support experimentation in different application domains. Such an universal shared experimentation platform would have a strong positive impact on adoption of applied research method, as currently supported by testbeds in networking and cloud research, in other research fields. Though facilitated by the proposed framework, this vision requires further research work in theory as well as application development.
Smart Condition Monitoring for Industry 4.0 Manufacturing Processes: An Ontology-Based Approach
Published in Cybernetics and Systems, 2019
Qiushi Cao, Franco Giustozzi, Cecilia Zanni-Merk, François de Bertrand de Beuvron, Christoph Reich
Normally, large-scale ontologies are not easy to manage and maintain. To cope with this issue, designing an ontology into separate knowledge components is an appropriate way to enhance its reusability and extensibility (d’Aquin et al. 2009). This design methodology is called ontology modularization, which stands for the method of structuring an ontology into different modules. In this work, we follow the ontology partitioning and module extraction approaches in d’Aquin et al. (2009), and structure our ontology into three modules: the Manufacturing Module, the Context Module, and the Condition Monitoring Module.