Abstract The challenge of using structured methods to represent knowledge is a well-documented issue in conceptual modeling and has been the focus of extensive research. It is widely recognized that adopting modeling patterns offers an effective structural approach for designing conceptual models. Patterns, in this context, refer to generalizable, recurring structures that provide solutions to common design problems. They significantly enhance both the understanding and improvement of the modeling process. Numerous experimental studies have demonstrated the undeniable value of using patterns in conceptual modeling. Despite this, the task of identifying patterns in conceptual models remains highly complex, and there is currently no systematic method for pattern discovery. To address this gap, this paper proposes a general approach for discovering frequent structures in conceptual modeling languages as a means to support pattern identification. Specifically, we focus on uncovering recurring structures that reflect the usage patterns of a given conceptual modeling language. 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 , such as graph visualization , graph clustering , and graph transformation . 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.

Abstract Conceptual modeling research is increasingly investigating the application of artificial intelligence (AI) and machine learning (ML) to automate tasks like model creation, completion, analysis, and processing. This trend also applies to enterprise architecture (EA) research. In contrast to its neighboring disciplines, such as business process management, EA lacks proper guidelines, patterns, and best practices to create high-quality EA models. A currently limiting factor for conducting AI-based research to bridge these gaps is the scarcity of openly available models of adequate quality and quantity. With this paper, our aim is to address this limitation by introducing the extended EA ModelSet , a curated and FAIR repository of enterprise architecture models represented in the ArchiMate modeling language that can be used by the research and practitioner community. We report on our efforts to build the EA ModelSet and elaborate on exemplary future empirical and ML-based research that can facilitate the dataset. We hope that this paper sparks a community effort toward the further development and maintenance of the EA ModelSet.

Enterprise Architecture offers guidelines for the coherent, model-based design and management of enterprises. EA models provide a layered, integrated, and cohesive representation of the enterprise, enabling communication, analysis, and decision making. With the increasing size of EA models, automated analysis becomes essential. However, advanced model analysis is neither incorporated in current EA methods like ArchiMate nor supported by existing EA tools like Archi. Knowledge Graphs (KGs) can effectively organize and represent knowledge and enable reasoning to utilize this knowledge, e.g., for decision support. This paper introduces a model-based Enterprise Architecture Knowledge Graph (EAKG) construction method and shows how starting from ArchiMate models, an initially derived EAKG can be further enriched by EA-specific and graph characteristics-based knowledge. The introduced EAKG entails new representation and reasoning methods applicable to EA knowledge. As a proof of concept, we present the results of a first Design Science Research Cycle aiming to realize an Archi plugin for the EAKG that enables analysis of EA Smells within ArchiMate models.