# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 4 Summary  
In this section, we summarize the most relevant findings of knowledge representation systems, reviewed in our survey (Section 3), in which ontologies have been used as a knowledge artifact. We investigated the approaches that enable the robots to have semantic skills. For this, ontology-based knowledge representation approaches that endow the robots with semantic understanding were explored and evaluated based on seven research questions and criteria. The first criterion concentrates on the application domain of the selected robotic KRS in which six robotic KRSs used for domestic tasks, two developed for medical robotics, while the remaining two designed for industrial robotics have been \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**KRS** & **Research Gaps/ Limitations** \\ \hline
**KnowRob** & Most of the works, however, focused on manipulation tasks only \\ \hline
**OROSU** & There is still a need for improvement for aligning medical and robotic ontologies due to the use of different upper ontologies \\ \hline
**CARESSES** & The proposed usage of CARESSESS cultural knowledge at a large scale might be challenging in robots with a strong bias; its cultural KB is built by hand with the help of experts \\ \hline
**PMK** & Although it provides general knowledge, some concepts are not well defined such as context-aware temporal and spatial relations, sensors’ knowledge, and task representation \\ \hline
**SARbot** & In a disaster SAR scenario, there is still a need to study the task planning for multiple heterogeneous robots in an uncertain environment \\ \hline
**IEQ** & To implement the IEQ, the environment should be robot-friendly, and the the user should have the correct pronunciation for speech interaction with the robot \\ \hline
**Smart Rules** & Some limitations of SmartRules include: the assumption of IoT objects known in advance; it does not deal with novelty autonomously; it needs a better method to bridge the semantic gap between entity descriptions and their representation; it does not support reasoning in the unfamiliar model using the current states of SmartRules and \#Concept \\ \hline
**ARBI** & It requires extending the knowledge model to support a more socially relatable user experience \\ \hline
**Worker-cobot** & Its case study addresses only a few operation resources \\ \hline
**APRS** & The APRS project shows that despite significant efforts to improve agility in manufacturing kitting, more research is needed to deal with action failures. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: KRSs: Limitations. explored. The other three criteria discuss the key idea and contribution, development tools, and architecture. The fifth criterion is concerned with the ontological scope. KnowRob, which is the first one, uses ontologies for KRS and is in fact well documented; meanwhile, other KR systems are more recent. OROSU is the first KRS that integrates robotic and surgical ontologies; however, it is not fully accessible and lacks the definitions to understand the terms. CARESSES's cultural KB contains hand-crafted ontologies that tend to be inflexible and complex, which needs to be addressed to make it more effective and efficient in sharing and porting of knowledge in the cultural domain. The sixth criterion is related to the reasoning scope of the ontologies for each robotic KRS, which demonstrates the support of the cognitive capabilities of the robot. Our review showed that ontology-based knowledge representation and reasoning endow the robots with a wide range of cognitive capabilities (e.g., including perceptional capabilities to estimate human emotions, recognize object and places, detect human actions and activities, planning and performing search and rescue operations). Based on the prior studies in this article, it can be reported that the efforts in these KRSs should be continued with the latest updates for reuse, and KRSs should be extended with more options to implement in new applications.