# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 3 Analysis of Knowledge Representation Systems
### Development Tools
\begin{table}
\begin{tabular}{l p{284.5 pt}} \hline \hline
**KRS** & **Solution/Contribution** \\ \hline \multirow{3}{*}{**PMK**} & Formalizes ontological representation for semantic perception and manipulation. \\  & Performs TAMP by including sensing information, semantic, geometric, and spatial reasoning with ontological concepts. \\ \hline \multirow{3}{*}{**SARbot**} & Enables robotic search and rescue operations in an unknown environment by: \\  & Endowing the robot with high-level control and supporting decision making using the ontology. \\ \hline \multirow{3}{*}{**IEQ**} & Develops the IEQ ontology for monitoring indoor environment quality. \\  & Integrates IEQ with post-occupancy evaluation (POE). \\  & Enables the robot to make appropriate suggestions based on the individual’s preferences to control the indoor temperature. \\ \hline \multirow{3}{*}{**Smart Rules**} & Overcomes the limitations of standalone robots. \\  & Develops a context-aware IoRT knowledge representation system. \\  & Allows human-robot interaction in both the physical and cyber world. \\  & Deploys rules based on an environment ontology. \\ \hline \multirow{3}{*}{**ARBI**} & Uses symbolic representation for a better understanding of the environment. \\  & Develops an integrated model based on the ontology. \\  & Endows the robot to perform human-robot interactive services. \\ \hline \multirow{3}{*}{**Worker-cobot**} & Enables the robot to work in collaboration with human workers while sharing the same manufacturing unit. \\  & Achieves agile manufacturing through ontology-based MAS and BRMS. \\ \hline \multirow{3}{*}{**APRS**} & Introduces an ontology-based model for kitting process. \\  & Empowers the robot with agility. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont. The SmartRules framework in [74] allows IoT systems to use and react according to the contextual knowledge and abstract the heterogeneity of the MOs. These SmartRules were developed using two languages in which context management rules are defined by the SmartRules sub-language. \(\mu\)-Concept is a language used in the ontology-based knowledge representation system [90] to model the context, in particular objects and actions between them in the IoRT environment. The \(\mu\)-Concept language contains three main constructs called concepts, properties, and individuals. It specifies the IoRT environmental ontology with entities such as physical objects and actions, and its syntax is based on the RDFS XML schema. These two sub-languages provide efficient reasoning on MOs to generate high-level contextual information or actions. SmartRules' sub-language specifies context management rules that allow the reasoner to perform the inference process, known as matching. It is composed of if-then production rules in which \(\mu\)-Concept constructs are used as predicates that are stored in variables. Compared to OWL, which uses the open world assumption (OWA) to reason, both SmartRules and \(\mu\)-Concept use the closed world assumption (CWA) for reasoning. ARBI [75] uses SPARQL statements and Apache Jena ([[https://jena.apache.org](https://jena.apache.org)]([https://jena.apache.org](https://jena.apache.org))) to implement the knowledge query and inference functions along with ROS-based actuator functioning. Its context reasoner is based on Prolog, while the preconditions for a certain action are described as OWL-based ontological restrictions on property. In worker-cobot [76], the Holonic Control Architecture (HCA) was developed, which was implemented via the Java Agent Development Environment (JADE) [91], while it used the ontology-based Agent Communication Language (ACL) [92] for message exchange between JADE agents. The models in the APRS kitting [77] framework were defined in the XML Schema Definition Language (XSDL) and the Web Ontology Language (OWL). The development tools used in each KR system are listed in Table 5.