# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.2 Standard Logic-Based Formalisms  
In terms of the declarative aspects, standard logic-based formalisms are high as they offer rigorous, well-defined formal languages to express knowledge, providing precise and unambiguous representation. These formalisms guarantee accuracy, ensuring that conclusions drawn from the knowledge base are logically correct and allow for precise and detailed descriptions of the domain, capturing fine-grained distinctions and enforcing precision via explicit constraints or rules. Nonetheless, while comprehensive within the \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Citeria Category**} & \multirow{2}{*}{**Quality Criteria 1**} & \multicolumn{2}{c}{_Bayesian/_Causal_2} & \multirow{2}{*}{_Standard Logic_} & \multirow{2}{*}{_Rules_} & \multirow{2}{*}{_Graphs 3_} & \multirow{2}{*}{_Trees 3_} & \multirow{2}{*}{_Simulation_} & \multirow{2}{*}{_Instance_} & \multicolumn{2}{c}{_Neural Networks_} & \multirow{2}{*}{_LLM/d_} \\  & & & & & & & & & & & \\ \hline \multirow{8}{*}{Declarative} & _Formalism_ & High & High & High & High & Medium & Medium & Low & Low \\ \cline{2-11}  & _Accuracy_ & High & High & High & High & High & High & High & High & High & Medium \\ \cline{2-11}  & _Precision_ & High & High & High & High & Medium & High & Medium & Medium & Medium \\ \cline{2-11}  & _Coverage_ & Medium & Medium & Medium & High & Medium & High & Medium & Medium & Medium \\ \cline{2-11}  & _Generalizability_ & Medium & Medium & Medium & Medium & Medium & Low & Medium & High \\ \cline{2-11}  & _Explainability_ & Medium & High & High & High & High & Medium & Medium & Low & Low \\ \hline \multirow{8}{*}{Procedural} & _Certainty_ & High & Medium & Medium & Medium & Low & Medium & Low & Medium \\ \cline{2-11}  & _Consistency_ & High & Medium & Medium & High & High & Medium & Medium & Medium \\ \cline{1-11}  & _Confidence_ & High & Low & Medium & Medium & Low & Medium & Low & Medium & Medium \\ \cline{1-11}  & _Expendability_ & High & High & High & High & Medium & Low & Low & Low \\ \cline{1-11}  & _Explainability_ & Medium & Low & Low & High & Medium & Medium & High & Medium & Medium \\ \hline \hline \end{tabular}  
* It should be noted that knowledge representation is strongly associated with and inherent to the reasoning process. For example, symbolic reasoning requires a symbolic representation; therefore, symbolic reasoning based on knowledge graphs requires first transferring the graph to a symbolic form. 2 Belief networks are a subcategory of graphs that allow for Bayesian reasoning. However, graphs are not a prerequisite; rather, the main knowledge representation is the joint probability tensor. 3 Reasoning in graphs and tree structures implies traversing through the graph or traversing through the tree from root to leaf nodes.  
\end{table} 
Table 2: Evaluation Results of Knowledge-based Approaches. defined logical constraints, they might struggle to generalize beyond the scope and/or handle incomplete knowledge beyond the logic of the defined rules. On the other hand, they inherently provide transparent reasoning processes, offering clear and explainable justifications via logical rules. In terms of procedural qualities, standard logic ensures strict adherence to formal rules, allowing for the detection and resolution of conflicts, and ensuring coherence in the knowledge base. However, it provides no mechanisms to deal with inconsistencies due to non-monotonicity where contradicting knowledge may exist. They represent knowledge deterministically, without encoding uncertainty and without assigning confidence levels to conclusions or assessing the reliability of inference results, which can be both an advantage and a limitation in handling uncertain domains. Standard logic formalisms are highly expandable, deriving new knowledge from existing facts or rules, but may fail with scalability when dealing with vast amounts of knowledge or complex relationships due to computational complexity.