# SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
## 5 Implementation and Evaluation Using an Example
### A Use Case on Chemical Pollution Monitoring
#### 5.2.2 Ontology Extensions for the Scenario  
To represent the specific scenario described in Section 5.2.1, some ontology elements defined in the SWARMs ontology need to be instantiated. Besides, additional ontology extensions, such as concepts and object properties, need to be added in order to provide a complete modeling for the scenario. Specifically, The _mission1_: _inspect the water column rgn1_ is an individual of concept Mission. Its goals (e.g., _goal1_ and _goal2_) can be inserted as instances of concept Goal. The mission plan _mp1_ generated by the MMT is an instance of concept Mission. The _mp1_ would include a set of tasks which could be created as instances of concept Task and refined as instances of concept LowLevelTask or HighLevelTask depending on their level. Vehicles involved in this mission, _auv1_ and _auv2_, are individuals of concept AUV defined in the robotic vehicle domain-specific ontology. Different capabilities provided by vehicles, such as _move_, _take videos_, _measure the concentration of H\({}_{2}\)S_, can be instances of concept Service and be linked to concept AUV through an object property _providedBy_. Sensors like _H\({}_{2}\)S probe_, _underwater camera with lighting system_, and _side-scan sonar_, are created as instances of concept Sensor. The detected chemical material, H\({}_{2}\)S, can be populated as an instance of concept Pollutant (as a sub-class of other objects concept) within the environment recognition and sensing domain-specific ontology.  
It is worth noting that some environmental context data, useful to describe this scenario, including, weather, depth, concentration level, and spread speed, need to be captured and modeled into the SWARMs ontology in order to fully meet the application-specific modeling requirements. In addition, uncertainty, inherent to instances of context data, should also be modeled. As mentioned previously, the MEBN theory is adopted to model and reason about context uncertainty. Therefore, in order to model the application-specific information, an MEBN model, shown in Figure 10, is created using the UnBBayes tool and formalized in a probabilistic ontology ([[https://archive.org/download/purl_xinli](https://archive.org/download/purl_xinli)]([https://archive.org/download/purl_xinli](https://archive.org/download/purl_xinli))) using PR-OWL constructs.  
The probabilistic ontology, shown in Figure 10, models that the weather and wind speed in the investigated sea region can affect the currents in the same region. Currents, along with the concentration of H\({}_{2}\)S, depth of H\({}_{2}\)S, and earthquake occurrence in the sea region, are the influential factors to estimate the emergency level of the polluted sea region. The probabilistic ontology uses ontology constructs defined in the PR-OWL ontology to annotate probability information of those factors. For instance, the joint probability distribution of emergency level, shown in Figure 11, can be annotated using OWL constructs shown in Figure 12. Specifically, the probability distribution of EmergencyLevel is annotated as a string by using a _hasDeclaration_ datatype. For the demonstration purpose, the probability information shown in Figure 11 is directly provided by marine experts.  
Figure 11. The joint probability distribution of emergency level.  
Figure 10. The probabilistic ontology for estimating emergency level of pollution in an MEBN theory.  
Incorporating with the probabilistic enrichments, the SWARMs ontology is able to accommodate new application requirements and provide a comprehensive representation of this scenario and also its context uncertainty.