# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Analysis Measurements  
For each one of the criteria, the following qualitative measurements are principally defined:  
* _Knowledge formalism_--The choice of knowledge formalism can be assessed based on its expressive power, syntactic and semantic clarity, and adherence to the established standards. Evaluation involves considering how well the formalism captures the complexities of the domain, supports precise representation, and aligns with the requirements of the application. An indication of the level of knowledge formalism would be given by the percentage of relevant standards, like expressiveness, clarity, etc., met by the knowledge formalism. * _Knowledge accuracy_--Accuracy in a formalism is measured by assessing the correspondence between the represented knowledge and the actual state of affairs. This can be performed via continued evaluations of the formalism's performance in making correct inferences or predictions within the domain. An indication of knowledge accuracy would be given by the percentage of predictions or inferences that align with real-world data or expert assessments. * _Knowledge precision_--Precision can be evaluated by examining the level of detail in the representation, such as the granularity of concepts, relationships, or constraints. A highly precise representation contains specific rules, attributes, or axioms that leave little room for ambiguity. An indication of knowledge precision would be given by the proportion of highly detailed rules or attributes in the knowledge base. * _Knowledge coverage_--Coverage can be evaluated by comparing the knowledge representation to a comprehensive domain ontology or by assessing whether it includes all relevant concepts, entities, and relationships needed to address specific use cases or queries within the domain. An indication of knowledge coverage would be given by the extent to which the knowledge base covers the domain ontology's concepts. * _Knowledge generalizability_-- Generalizability can be assessed by examining the performance and applicability of the knowledge representation across a range of related domains or use cases. It involves testing whether the knowledge and reasoning methods can be extended to diverse contexts effectively. An indication of knowledge generalizability would be given by the representation's effectiveness when applied to different domains. * _Knowledge explainability_--Explainability is measured by examining the degree to which formalism facilitates methods for generating human-readable explanations of the reasoning process, making it comprehensible to users or stakeholders. An indication of knowledge explainability would be given by the normalized length of the path from premises to conclusions of the reasoning processes. * _Knowledge certainty_--Certainty can be quantified using probabilistic measures or certainty factors that assign degrees of belief or confidence to individual pieces of knowledge within the representation. An indication of knowledge certainty would be given by the probability of correct/incorrect individual knowledge pieces over the total of knowledge pieces. * _Knowledge consistency_--Consistency can be assessed by checking whether the representation provides mechanisms to detect and resolve conflicts or contradictions, ensuring that the knowledge remains internally coherent. An indication of knowledge consistency would be given by the consistency of knowledge when comparing coherent pieces, i.e., non-conflicting knowledge pieces against the total. * _Knowledge confidence_--Confidence in a formalism can be evaluated by examining its provisions for attaching confidence scores or measures to conclusions and assessing the mechanisms for assessing the reliability of knowledge sources. An indication of knowledge confidence would be given by statistical confidence or the confidence ratings given by users or experts.