Detecting Resemblances in Anti-pattern Ideologies using Social Networks

A key argument for modelling knowledge in ideologies is the simple reuse of the facts. However, nearby reliability checking, current ideology engineering tools give only essential functionalities for analyzing ideologies. Since ideologies can be considered as graphs, graph analysis techniques are an apt answer for this necessity. The anti-pattern ideology has been recently proposed as a knowledge base for SPARSE, an intelligent system that can detect the anti-patterns that exist in a software project. However, apart from the excess of anti-patterns that are intrinsically informal and vague, the data used in the anti-pattern ideology itself is many times inexactly defined. We exemplify in this paper the benefits of applying social networks to ontologies and the Semantic Web and discuss which research themes happen on the edge between the two particular fields. Particularly, we confer how different ideas of centrality portray the core content and structure of ontology