Summary of Mining Frequent Structures in Conceptual Models, by Mattia Fumagalli et al.
Mining Frequent Structures in Conceptual Models
by Mattia Fumagalli, Tiago Prince Sales, Pedro Paulo F. Barcelos, Giovanni Micale, Philipp-Lorenz Glaser, Dominik Bork, Vadim Zaytsev, Diego Calvanese, Giancarlo Guizzardi
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach aims to address the problem of discovering frequent structures in conceptual modeling languages by implementing an exploratory tool that integrates a frequent subgraph mining algorithm with graph manipulation techniques. This tool processes multiple conceptual models and identifies recurrent structures based on various criteria, validating its effectiveness using two state-of-the-art curated datasets: one consisting of models encoded in OntoUML and the other in ArchiMate. The primary objective is to provide a support tool for language engineers to identify both effective and ineffective modeling practices, enabling the refinement and evolution of conceptual modeling languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an approach to discover frequent structures in conceptual modeling languages. It develops an exploratory tool that integrates a subgraph mining algorithm with graph manipulation techniques. The tool processes multiple models and identifies recurrent patterns based on criteria. This is validated using two datasets: OntoUML and ArchiMate. The goal is to provide a support tool for language engineers to improve conceptual modeling languages. |
