Mining Frequent Structures in Conceptual Models

The problem of using structured methods to represent knowledge is well-known in conceptual modeling and has been studied for many years. It has been proven that adopting modeling patterns represents an effective structural method. Patterns are, indeed, generalizable recurrent structures that can be exploited as solutions to design problems. They aid in understanding and improving the process of creating models. The undeniable value of using patterns in conceptual modeling was demonstrated in several experimental studies. However, discovering patterns in conceptual models is widely recognized as a highly complex task and a systematic solution to pattern identification is currently lacking. In this paper, we propose a general approach to the problem of discovering frequent structures, as they occur in conceptual modeling languages. As proof of concept for our scientific contribution, we provide an implementation of the approach, by focusing on UML class diagrams, in particular OntoUML models. This implementation comprises an exploratory tool, which, through the combination of a frequent subgraph mining algorithm and graph manipulation techniques, can process multiple conceptual models and discover recurrent structures according to multiple criteria. The primary objective is to offer a support facility for language engineers. This can be employed to leverage both good and bad modeling practices, to evolve and maintain the conceptual modeling language, and to promote the reuse of encoded experience in designing better models with the given language.