# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 3 Analysis of Knowledge Representation Systems
### Architecture  
In this subsection, we give a review of the architecture for each selected KRS, with a discussion of their main components, summarized in Table 6.  
The KnowRob 2.0 [68] architecture consists of the interface shell, logic-based language, and hybrid reasoning shell, which are centered on the symbolic representation of the ontology. It operates within the perception-action loop of the control system in the embodiment of the robotic agent: the interface shell contains question answering, perception, learning, and recorded episodic memory. It uniforms the query answering system to execute the assigned task by referring to the captured image data, observing the state of the object, and inferring the appropriate motion parameters. The queries related to the manipulation tasks require parameterization of the motion and data structure along with the function calls. \begin{table}
\begin{tabular}{l l} \hline \hline
**KRS** & **Development Tools** \\ \hline
**KnowRob** & OWL, SWI-Prolog \\ \hline
**OROSU** & OWL \\ \hline
**CARESSES** & OWL, Bayesian Networks \\ \hline
**PMK** & OWL, SWI-Prolog \\ \hline
**SARbot** & OWL, SWRL, JESS \\ \hline
**IEQ** & Normative \\ \hline
**SmartRules** & SmartRules sub-language, \(\mu\)-Concept \\ \hline
**ARBI** & OWL, Prolog, SPARQL \\ \hline
**Worker-cobot** & JADE, ACL \\ \hline
**APRS** & XSDL \\ \hline \hline \end{tabular}
\end{table} 
Table 5: KRSs: development tools. The logic-based language facilitates a hybrid reasoning shell by providing descriptions for entities such as objects, their parts, and environments. It also uses control-level data structures for grounding the symbolic expressions and provides ontologies-based heterogeneous representations. The hybrid reasoning shell implements the knowledge using multiple methods including key components that are data structures, robotics algorithms for inverse kinematics, and collision-free motion planning for finding accurate paths to the precise position of the object. The OROSU [69] architecture is based on the combination of multiple ontologies, while its base ontology partially relies on the 1872-2015 IEEE Standard Ontologies for Robotics and Automation [93]. It is among the first frameworks that merges robotic and medical ontologies for surgical robotics. The CARESSES [70] architecture consists of three main modules: the cultural knowledge base (CKB), the culturally sensitive planning and execution, and the culture-aware human-robot interaction, which are integrated into universAAL [94]. The CKB is the core of its framework architecture. The PMK [71] architecture consists of four major components known as the perception module, PMK framework, and TAMP planning and execution module. The perception module detects the world entities using tags, and then, their identified poses and IDs are used for building IOC knowledge. The PMK framework offers reasoning predicates for perception and object features along with situation analysis. The TAMP module is used to plan the task by the fast-forward task planner [95] and combines it with physics-based motion planning [96]. Finally, the execution module performs the assigned task. The SARbot [72] architecture divides the robot control into three levels. The motors and sensors are controlled by the low-level control, while environment perception, SLAM, and navigation are handled by the middle-level control, and smart decisions of the robot are made by the high-level control. The IEQ [73] architecture for providing suggestions by human-robot interaction consists of seven major elements: the knowledge base (KB), which is the central storage for all the robot's knowledge containing the current instance of the domain ontology that is related to the current monitoring session; the dialog and speech recognition module; the suggestion module; the Light_Sound data acquisition module.