# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 3 Analysis of Knowledge Representation Systems
### Ontology Scope  
This subsection discusses the scope of each KRS based on the ontology in the domain of robotics. An ontology is best defined as a conceptualization-based formal representation of knowledge that includes the explicit specification of concepts and objects, other entities presented in the environment and relationships among them, as well as tasks and actions. We divide this section into two parts. The first part gives an overview of the ontologies used for each selected KR system and summarizes them in Table 7. The second part deals with classifying KR systems according to their major ontological components that include the object (Section 3.5.1), the map of the environment (Section 3.5.2) and the task and action (Section 3.5.3), which are summarized in Table 8.  
Starting from the overview of the ontologies, KnowRob [68] uses multiple ontologies to allow communication for information sharing among the agents. Its main ontology states robotic agents, their connected body parts, tasks, actions, behavior, objects with their parts, and the environment. It provides relevant background knowledge in the form of rules with additional axioms. Its ontological knowledge representation approach consists of four main knowledge bases in which the inner world knowledge base contains CAD and mesh models of real-world objects with physics simulation; the virtual knowledge base from the data structure is computed on demand; the logic knowledge base abstracts the data from sensors, control commands, and events such as grasping; while the recording of these events and experiences of the robotic agents is stored in the episodic memory knowledge base. The OROSU [69] ontology uses the knowledge from the CORA ontology [93] for defining the general concepts of medicine, human anatomical ontology, SNOMED-CT [98], and the KNOWROB framework [14; 99]. It adopts the robot parts (PARTS) ontology along with its interaction with the suggested upper merged ontology (SUMO). The extensive vocabulary and flexibility of SUMO make it a top-level ontology [100]. CARESSES [70] provides human activity recognition and presents cultural information using ontologies. Its knowledge is contained in a modular ontology structure that combines bottom-up and top-down approaches. Its ontology for culturally competent robots relies on three layers with four major elements, TBox, CBox, PBox, and algorithm. The culture-generic knowledge layer contains the terminology (TBox, I) for representing information related to the robot, goal, action, and the grounding of values for all the cultures of the world, considered in KB. The culture-specific settings layer holds the assertions (CS-ABox, II) for representing information to depict the cultural background of the individual, which the robot can use when certain information is not available. The person-specific settings layer keeps the assertions (PS-ABox, III) for representing the cultural identity, environment, and preferences of the assisted person that specify the appropriate behavior of the robot. The assessment and adaptation (A&A) algorithm identifies person-specific settings through dialog or observation. The terminology box (TBox) of the ontology is composed of classes and the general properties of the domain concepts such as data and object properties. A preliminary version [79] of PMK [71] was inspired by [4] for the indoor robot navigation task. It defines concepts by a hierarchy of ontologies with three layers, called the meta ontology, which describes concepts related to a physical object, the ontology schema, which represents domain-specific knowledge, and the ontology instance for storing information about a specific object such as object features. It follows the hierarchical schema to perform manipulation tasks using seven classes of three layers.