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

Keywords: Conceptual Modeling, Analytical models, Systematics, Machine Learning algorithms, Bibliographies, Semantics, Machine Learning
Astract: Conceptual models are essential in Software and Information Systems Engineering to meet many purposes since they explicitly represent the subject domains. Machine Learning (ML) approaches have recently been used in conceptual modeling to realize, among others, intelligent modeling assistance, model transformation, and metamodel classification. These works en-code models in various ways, making the encoded models suitable for applying ML algorithms. The encodings capture the models' structure and/or semantics, making this information available to the ML model during training. Therefore, the choice of the encoding for any ML-driven task is crucial for the ML model to learn the relevant contextual information. In this paper, we report findings from a systematic literature review which yields insights into the current research in machine learning for conceptual modeling (ML4CM). The review focuses on the various encodings used in existing ML4CM solutions and provides insights into i) which are the information sources, ii) how is the conceptual model's structure and/or semantics encoded, iii) why is the model encoded, i.e., for which conceptual modeling task and, iv) which ML algorithms are applied. The results aim to structure the state of the art in encoding conceptual models for ML.

Keywords: Enterprise Architecture, Knowledge Graph, Modeling tool, ArchiMate, Archi
Astract: 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.

Keywords: Conceptual Modeling, Semantic Web, Ontology, Systematic Mapping Study, Web Knowledge Base, Knowledge Graphs, Linked Data
Astract: Conceptual models aim to represent real systems at a higher abstraction level. The Semantic Web intends to add meaning to any kind of data formats to arrive at linked data. Taken together, both of them help facilitate data processing and integration for humans as well as for machines. This thesis analyzes the publication landscape at the intersection of conceptual modeling and Semantic Web in the form of a systematic mapping study (SMS). In line with the SMS, the research scope is defined, the search queries are executed, and the publications are screened from an initial number of 5107 to finally 484 papers. Then publications are extracted and mapped according to a series of previously developed taxonomies. The extracted and refined data is analyzed in several analysis steps comprising bibliographical, content, combined taxonomy as well as research community analyses. Threats to validity, and implications for future research from this first SMS regarding the intersection of conceptual modeling and Semantic Web are additionally considered.