Keywords: Archi, ArchiMate, Enterprise architecture, Knowledge graph, Modeling tool
Astract: This paper presents the EAKG Toolkit that entails a new Knowledge Graph-based representation of enterprise architecture (EA) models and further enables reasoning on EA knowledge. Our developed EAKG Toolkit is unique in the sense that it i) transforms ArchiMate models into a KG representation – the Enterprise Architecture Knowledge Graph (EAKG), ii) visualizes the EAKG for interactive exploration, and iii) extends the EAKG with additional nodes and edges to visually represent detected EA smells.

Keywords: Conceptual Modeling, Graphical Language Server Protocol, Integrated Development Environment, Language Server Protocol, Modeling tools, Software Engineering
Astract: With the introduction of the Language Server Protocol (LSP), a fundamental shift has been observed in the development of language editing support for Integrated Development Environments (IDEs), such as VS Code, the traditional Eclipse IDE, or Eclipse Theia. LSP establishes a uniform protocol that standardizes the communication between a language client (e. g., an IDE like Eclipse) and a language server (e. g., for a programming language like Java). The language client only needs to be able to interpret and understand the protocol instead of the specific programming language. Likewise, the language server can focus on language support and does not need to consider the specifics of a respective IDE. This reduces the complexity of realizing language support on different editors and IDEs and enables smooth transitions from one IDE to another. LSP is an open and community-driven protocol that has been developed within the realm of the VS Code community, initiated and driven by Microsoft. The generic concept and architectural pattern of LSP enables widespread applications that go far beyond the realization of editing support for programming languages. This paper provides an introduction to LSP, describes its evolution and core characteristics, and delineates its potential for revolutionizing not only the IDE market but also other software systems, such as modeling tools.

Keywords: Conceptual Modeling

Keywords: Graph Neural Networks, Ontology-Driven Conceptual models, Representation Learning
Astract: Conceptual Models (CMs) are essential for information systems engineering since they provide explicit and detailed representations of the subject domains at hand. Ontology-driven conceptual modeling (ODCM) languages provide primitives for articulating these domain notions based on the ontological categories put forth by upper-level (or foundational) ontologies. Many existing CMs have been created using ontologically-neutral languages (e.g., UML, ER). Connecting these models to ontological categories would provide better support for meaning negotiation, semantic interoperability, and complexity management. However, given the sheer size of this legacy base, manual stereotyping is a prohibitive task. This paper addresses this problem by proposing an approach based on Graph Neural Networks towards automating the task of stereotyping UML class diagrams with the meta-classes offered by the ODCM language OntoUML. Since these meta-classes (stereotypes) represent ontological distinctions put forth by a foundational ontology, this task is equivalent to ontological category prediction for these classes. To enable this approach, we propose a strategy for representing CM vector embeddings that preserve the model elements’ structure and ontological categorization. Finally, we present an evaluation that shows convincing learning of OntoUML model node embeddings used for OntoUML stereotype prediction.

Keywords: Model-Based Construction