Mining Frequent Structures in Conceptual Models
🤖 AI Summary
The absence of automated, systematic approaches for modeling pattern recognition in conceptual modeling hinders improvements in knowledge representation and modeling quality.
Method: This paper introduces frequent subgraph mining—systematically applied to conceptual modeling for the first time—and proposes a cross-language (OntoUML/ArchiMate), multi-criteria structural pattern discovery framework. It integrates a gSpan variant with graph editing, graph isomorphism testing, and pattern abstraction techniques to build an extensible, exploratory analysis tool.
Contribution/Results: Evaluated on two authoritative datasets, the method successfully identifies highly reusable structural patterns. It demonstrates effectiveness in assessing modeling practices, supporting language evolution, and optimizing model quality—thereby filling a critical research gap in automated pattern mining for conceptual modeling.
Application Category
📝 Abstract
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, we implement our approach by focusing on two widely-used conceptual modeling languages. This implementation includes an exploratory tool that integrates a frequent subgraph mining algorithm with graph manipulation techniques. The tool processes multiple conceptual models and identifies recurrent structures based on various criteria. We validate the tool using two state-of-the-art curated datasets: one consisting of models encoded in OntoUML and the other in ArchiMate. The primary objective of our approach is to provide a support tool for language engineers. This tool can be used to identify both effective and ineffective modeling practices, enabling the refinement and evolution of conceptual modeling languages. Furthermore, it facilitates the reuse of accumulated expertise, ultimately supporting the creation of higher-quality models in a given language.
Problem
Research questions and friction points this paper is trying to address.
Mature Method
Structural Patterns
Knowledge Representation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pattern Recognition
Graph Algorithms
Model Optimization
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