# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 4 Knowledge-Driven Applications in Telecommunications
### Network Automation  
Intent-based network management has been introduced in telecommunications to offer automated control and intelligent optimization of complex networks [33, 34]. Intent-based networking (IBN) [35] and Intent-driven Networks (IDNs) have evolved from SDNs and NFVs management disciplines [36] as a successive evolution of policy-based networks (PBNs) [37]. Central to these management disciplines are the network policies governing the network operation. Intent-based operation extends previous approaches in that it first offers an abstraction layer expressing the desired outcome of the network service, and second, it automatically determines how the network should be configured in an efficient and reliable manner [38].  
As shown in Figure 4, IBN is characterized by a closed-loop cycle comprising four processes: (i) intent translation, where the abstracted intent is mapped to a network configuration; (ii) intent resolution, where configuration conflicts are resolved; (iii) intent actuation, where the requested service is provisioned; and (iv) intent assurance, where provisioned services are resilient to network changes. In its simplest form, IBNs require a translation of intents to network policies, and finally, to detailed network configurations that govern the infrastructure and the resources. More flexible and dynamic forms require the employment of intent-driven systems with associated reasoning engines.  
At the core of all flexible and dynamic intent-driven systems lies the crucial element of knowledge representation. This involves modeling intents in a way that is easily understood by humans while also being executable by machines. In this context, knowledge graphs have emerged as a widely adopted method for representing intents. These  
Figure 4: Intent-based automation process cycle.  
knowledge graphs encompass entities, relationships, and constraints, serving as structured repositories for intent definitions, essentially defining an ontology [39].  
However, it is essential to note that knowledge representation is just one aspect of the broader picture. Intent-driven systems rely on reasoning engines as their key components, which can take the form of rule-based logic, advanced semantic reasoning capabilities, or large language models. These engines play a pivotal role in interpreting intents, translating them, and breaking them down into specific network actions. They are specifically designed for tasks such as inferring intent decomposition, resolving conflicts, and optimizing configurations to ensure the provision of services aligned with the intended goals [40].  
Another distinctive characteristic of intent-driven systems is their adaptability. Combining knowledge-based systems with machine learning techniques allows these systems to evolve alongside changing network conditions and user requirements [41]. They can be designed to learn from historical data and user feedback, continuously enhancing their ability to comprehend and fulfill intents. Moreover, intent-driven systems contribute significantly to ongoing network improvements. They achieve this by automating routine tasks, optimizing resource allocation, and ensuring compliance with established policies. Their reliability is further bolstered by their ability to anticipate potential consequences of actions, foresee issues, and proactively mitigate them by selecting alternative network configuration solutions that align with the intended objectives.  
Advances in natural language understanding, explainable AI, and distributed knowledge graphs are promising future directions to further augment the capabilities of these intent-driven systems, making them indispensable in the face of growing network complexity.