# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 2 Related Works  
In this section, we review the literature related to ontology-based knowledge representation for cognitive robots. Cognitive robots are autonomous systems that are intelligent agents capable of performing a task with a high degree of autonomy in different application domains. To achieve semantic integration, ontologies work as the conceptual backbones for autonomous systems and offer several benefits such as [29]: interoperability through shared understanding of the problem domain; formalization to make shared understanding machine-processable; semantic representation to provide quality services in automatic robot systems. A domain ontology for autonomous systems (OASys) was developed by [30] for analysis, implementation, and run-time processes. It was designed to capture the knowledge from the system, environment, and task models to exploit cognitive control loops. The ontology-based conceptual model of OASys provided the support for both the description and generic development of the engineering process. It used four ontologies: the system ontology for including the elements to define the details of autonomous system; the ASys system ontology for defining the structure, behavior, and functions; the system engineering ontology for listing the engineering process related to the system and its software; the ASys system engineering ontology for addressing the additional ontological elements related to the engineering process of the autonomous system. In another study [31], an ontology-driven framework was designed. It was composed of OASys (as the domain ontology) and an ontology-driven engineering methodology (ODEM) for developing self-x mechanisms into autonomous robots. The OASys contained two layers [32] for representing higher and lower level ontological elements, which were  
Figure 3: Survey structure and its corresponding sections. used to address general and autonomous systems, respectively. The ODEM was more focused on generic engineering processes and development support for ontology-based autonomous systems [30]. Robotic systems can perform human-like tasks more flexibly if they can share knowledge among themselves and use crowdsourcing to tap human skills. One approach towards this direction is using an ontology as a cloud application. In a study, [PERSON] [33] presented the openEASE cloud engine ([[https://data.openease.org](https://data.openease.org)]([https://data.openease.org](https://data.openease.org))) with ontologies in the kitchen domain and execution logs from three robots operating in two kitchens. [PERSON] [34] made a seminal contribution towards the extension of existing ontological standards through the development of behavior-, action-, and capability-related ontological concepts, which are crucial to enhance the cloud robotics domain. There are many projects that use ontologies to represent vocabulary and knowledge acquired by robots in certain scenarios, such as kitting rehabilitation [35; 36]. Knowledge modeling (KMo) using ontologies focuses on making systems intelligent [37]. Ontology development depends on information artifacts that can be distinguished according to their purpose. [PERSON] [37] used model-based systems engineering (MBSE) as the central information artifact, which offers benefits from the conceptual to system development phase in the case of autonomous driving function. it demonstrated the possible improvements by knowledge modeling and ontologies in autonomous vehicle systems engineering. Ontologies address issues related to inconsistency and lack of domain knowledge. Recent developments in robotics have highlighted the need for conceptual knowledge about objects in a robot's environment to efficiently perform human-scale tasks, i.e., holding an object or closing a drawer [38]. Embodied robotic agents require additional knowledge related to body movement to perform these tasks in the real world. However, many AI action models do not provide this knowledge and only concentrate on pre-post conditions and sequences of actions.