# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 5 Discussion and Future Research Directions  
The comparison of the ontology-based KR systems in this article did not cover all the ontological components and cognitive capabilities of a KR-based robotic system, which is natural. To our knowledge, there is no fixed or definitive method that could completely address all the aspects of these kinds of ontologies in a review. However, considering their important aspects in our survey, we presented the summary of our evaluation results (described in Tables 8 and 11) for the knowledge base of ten systems in the robotic domain. Hence, the goal of our survey was to review novel, yet valuable recent research developments in ontology-based KR systems for roboticists.  
The analysis of ontology-based KR systems led us to the following potential areas for future research directions.  
* The researchers are more focused on the development of ontologies in the field of knowledge representation, while the development of the mechanism lags.
* In the future, more research is needed towards the standardization and efficient implementation of ontology-based knowledge representation systems.
* We believe that along with the ontologies, future studies should also aim at the development of efficient queries and reasoning mechanisms that might be applied to many distributed ontologies with limited resources. In this direction, future work is certainly required to achieve a sustainable solution with a sound understanding of resources and quality.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \# & **KRS** & **Applications** & **Idea and Contribution** & **Development Tools** & **Architecture** & **Ontology** & **Reasoning** & **Limitation(s)** \\ \hline
1 & KnowRob & & & & & & & & \\ \hline
2 & OROSU & & & & & & & & \\ \hline
3 & CARESSES & & & & & & & & KRS: \\ \hline
4 & PMK & \begin{tabular}{c} KRS: \\ KRS: \\ and \\ Domain \\ \end{tabular} & \begin{tabular}{c} Table 3: \\ KRS: \\ KRS: \\ Contribution \\ \end{tabular} & \begin{tabular}{c} Table 4: \\ KRS: \\ KRS: \\ Components \\ \end{tabular} & \
\begin{tabular}{c} Table 5: \\ KRS: \\ KRS: \\ Language \\ \end{tabular} &
\begin{tabular}{c} Table 6: \\ KRS: \\ KRS: \\ Limitations \\ \end{tabular} \\ \hline
7 & SmartRules & \begin{tabular}{c} Table 10: \\ KRS: \\ KRS: \\ Complements \\ \end{tabular} & \begin{tabular}{c} Table 8: \\ Reasoning \\ Complements \\ \end{tabular} & \begin{tabular}{c} Table 8: \\ Reasoning \\ Complements \\ \end{tabular} & \begin{tabular}{c} Table 8: \\ Reasoning \\ Complements \\ \end{tabular} & \
\begin{tabular}{c} Table 8: \\ Reasoning \\ Complements \\ \end{tabular} &
\begin{tabular}{c} Table 8: \\ Annotations \\ \end{tabular} \\ \hline
9 & Worker- &
\begin{tabular}{c} Table 9: \\ Robot \\ \end{tabular} & & & & & & \\ \hline
10 & ARPS & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Summary of the evaluation results of ontology-based knowledge representation systems in seven dimensions.  
Besides, future research can be more beneficial towards robot autonomy if the potential effects of ontology-based KR systems with context awareness are considered more carefully.
* Looking forward, future research should be continued in more realistic settings for the development of culturally competent KRSs, which will endow the robots the ability to perform various complex tasks in dynamic environments by understanding the culture-specific needs and preferences.  
To summarize our discussion, the results achieved in ontology-based KR systems for autonomous robotic systems so far seem tentative. Therefore, we highlighted the challenges and research gaps or limitations that require more work to be done in the future. It is expected that under the auspices of artificial intelligence, semantic web technology, and other accompanying ideas or visions, the development of this field with real-world robotic applications will continue to advance.