# Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
## 5 Evaluation
### Feature-Based Evaluation  
Feature-based evaluation is generally oriented toward evaluating the structural and schema aspects of ontologies to draw conclusions about their structure and schema quality. This evaluation has been adopted by different techniques, such as OntoClean, ONTOQA, and OntoMetrics. For our evolved ontology-based models, OntoMetrics [28] was used as a feature-based evaluation framework. It is a Web-based tool that consists of diverse metrics, such as base, schema, class, knowledge base, and graph metrics. In this work, we aimed to reuse two main metrics from OntoMetrics, called schema metrics and graph metrics, to determine the structure and schema quality with respect to the concepts, relations, and inheritance levels of the evolved ontology-based models. An overview of these metrics is discussed as follows:  
* The schema metrics assess the design of ontologies by calculating and comparing statistics about the concepts, inheritance levels, relation types, properties, and other elements. They stand for searching for schema-related errors such as recursive definitions, unconnected concepts, missing domains or ranges, and missing inverse relations. The most essential and significant metrics of the schema category are the following: the Inheritance Richness (IR), Relationship Richness (RR) and Inverse Relations Ratio (IRR).
* The IR is a way to measure the overall levels of the distribution of concepts of the ontology's inheritance tree. It is known as the average number of sub-concepts per concept to describe how the concepts are distributed across the different levels of the ontology and thus distinguish shallow from deep ontologies. A relatively low IR result would correspond to a deep or vertical ontology that covers its targeted environment in a very detailed way, while a high result, by contrast, would reflect a shallow or horizontal ontology that tends to represent a wide range of general concepts with fewer levels.
* The RR examines the existing relations within an ontology to reflect the diversity of relationships. It is calculated as the fraction of the number of non-taxonomic relations, specifically the object properties, and the total number of sub-concepts and non-taxonomic relations in the ontology. The RR result is a number between 0 and 1, where a high value closer to 1 indicates that the ontology is rich and contains a variety of non-taxonomic relations, while a small RR value closer to 0 indicates that the ontology mostly consists of subsumption relations.
* The IRR illustrates the ratio between the inverse non-taxonomic relations and all non-taxonomic relations. Lower values for this metric indicate a deficiency in the definition of inverse non-taxonomic relations in the ontology.
* Graph metrics are also known as structural metrics, where the taxonomy of ontologies is analyzed. These metrics calculate the cardinality and depth of the ontology structure in terms of the absolute and average depth, breadth, and so on. The depth metric that consists of an absolute and average is associated with the cardinality of the paths. The breadth metric, which is represented by the absolute and average, expresses the cardinality of the levels. The value of these different parameters in the graph metrics depicts the effectiveness of an ontology structure.