# A Comprehensive Survey on Knowledge-Defined Networking
## 3 Knowledge Plane
### Knowledge Composition Plane
#### 3.4.1 Composed Knowledge Representation  
The composed knowledge can be represented mainly in four different forms, which are described in the following sub-sections. Figure 4 depicts different formats in which knowledge can be represented. Resource Description Framework (RDF)  
RDF is the format in which generated knowledge is basically represented. This basic knowledge representation can also be used to represent the composed knowledge. However, this format is not recommended, as it is difficult to make decisions directly from the knowledge that appears in this format, as knowledge appears in basic triplet form and is not classified into classes, as evident from the example in Figure 4a. Therefore, it is recommended to represent the composed knowledge in any other format, such as a knowledge graph, ontology, or knowledge interchange format, which can be queried easily or used readily for making decisions. Figure 4: A sample of knowledge represented in different knowledge models. (**a**) Knowledge represented in Resource Description Framework (RDF) format. (**b**) Knowledge represented as a Knowledge graph. (**c**) Knowledge represented as an ontology. (**d**) Knowledge represented in Knowledge Intermediate Format (KIF). A knowledge graph represents the composed knowledge in a structured and machine-readable format. It consists of entities/nodes (representing concepts) and edges (representing relationships between the concepts), as shown in Figure 4b. Knowledge graphs are used to represent complex relationships between entities and can be used for semantic reasoning. One of the key benefits of using a knowledge graph in KDN is that it allows for flexible reasoning about the network. The graph structure enables sophisticated queries that can traverse multiple levels of abstraction. However, knowledge graphs can be limited in their ability to represent complex relationships and hierarchies [202]. Knowledge Graph Schema (KGS), collections of rules and standards that describe the structure of a knowledge graph, may be used to generate knowledge graphs. They offer a standardized method for representing entities, connections, and characteristics in a knowledge graph, assisting in the maintenance of consistent, accurate, and simple-to-understand data. A KGS can include a variety of components, such as classes, properties, and restrictions. An example of a KGS is the RDF Schema (RDFS) language, which provides a vocabulary for describing classes, properties, and their relationships [203]. Ontologies  
Ontologies define a set of concepts and their relationships that describe a particular domain. The Web Ontology Language (OWL) or Ontology Inference Layer (OIL) can be used to model and describe ontologies. OWL builds on knowledge that appears in the form of RDF to create a complex knowledge structure by clustering knowledge into domains (classes), as shown in Figure 4c. Thus, generated knowledge by machine learning models that appears in RDF format can be readily converted into composed knowledge as an ontology using OWL [204]. OWL has basic elements such as classes, properties, individuals (class instances), restrictions, and annotations. On the other hand, OIL extends RDF and provides a lightweight ontology language that consists of basic elements such as classes, properties, individuals, restrictions, and inference rules. However, OIL is less expressive than OWL, which means that OWL is capable of representing a wider range of concepts and relationships due to its richer syntax [205]. OWL has a richer syntax, as it allows the definition of property characteristics, property restrictions, datatype properties, etc. The use of ontologies in KDN enables the network to reason about the current state of the network and to make decisions based on that knowledge. Ontologies can be used to capture domain-specific knowledge, which can then be shared and reused across different network applications. However, ontology representation can be limited in its ability to represent uncertainty and ambiguity [206].