Mining Frequent Structures in Conceptual Models
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913625969524736 |
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| author | Fumagalli, Mattia Sales, Tiago Prince Barcelos, Pedro Paulo F. Micale, Giovanni Glaser, Philipp-Lorenz Bork, Dominik Zaytsev, Vadim Calvanese, Diego Guizzardi, Giancarlo |
| author_facet | Fumagalli, Mattia Sales, Tiago Prince Barcelos, Pedro Paulo F. Micale, Giovanni Glaser, Philipp-Lorenz Bork, Dominik Zaytsev, Vadim Calvanese, Diego Guizzardi, Giancarlo |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_07129 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mining Frequent Structures in Conceptual Models Fumagalli, Mattia Sales, Tiago Prince Barcelos, Pedro Paulo F. Micale, Giovanni Glaser, Philipp-Lorenz Bork, Dominik Zaytsev, Vadim Calvanese, Diego Guizzardi, Giancarlo Artificial Intelligence 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. |
| title | Mining Frequent Structures in Conceptual Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.07129 |