Metric Method for Long Life Semantic Applications

Abstract

Ontology is the core of the semantic applications, so the quality of it has a direct proportion with Ontology. It impacts directly on the long life of the semantic applications. There are different negative effects in the design of Ontology' classes as Blob, Lazy Class, Large Class, and Singleton. These negative effects called bad smells. However, detecting smells is not supported in any Ontology editors. This paper proposes a metric method called ONTOPYTHO. It can detect classes' smells automatically from Ontology models even if it is a Big Ontology. We programed ONTOPYTHO via Python and SPARQL languages. We evaluated the proposed method ONTOPYTHO by applying it on twelve publicly OWL Ontology projects. The detected smells appeared 117495 times in the twelve projects. The results showed that both the size and the number of classes of OWL Ontology has no effect in the presence of the smells. The results also showed that 69.24% of the classes are lazy classes. This means that big OWL Ontologies are not big in their nature, but because of theses lazy classes. The proposed method is the first method that detects Lazy class smell in the design of big OWL Ontology. In our random sample of big Ontologies, Lazy class smell appears approximately 99.8% of the smells.

The Malware Detection Approach in the Design of Mobile Applications

“…The previous work, [31], introduced 40 types of design anti-patterns that formed the basis of design anti-pattern detection approaches [32][33][34][35]. The approach in [36] presented the ONTOPYTHO technique to detect smells and anti-patterns on the design of OWL Ontologies based on a metric method via the semantic web query language, SPARQL, and Python programming language. In [37], the authors focused on detecting mobile applications' anti-patterns and they proposed a method using reverse engineering and a UML modeling environment.…”