OntoUML, an ontology-driven conceptual modelling language based on the Unified Foundational Ontology (UFO), has been demonstrated to have superior clarity in comparison to other commonly used modeling languages, as supported by empirical evidence. Furthermore, the use of RDF technology has led to the creation of the gUFO ontology, enabling the interoperability and reuse of RDF tools, thereby providing new opportunities to explore and evaluate large OntoUML/UFO models and their instances/data. This study utilizes the SPARQL query language to evaluate the quality of OntoUML models and develop queries for identifying and retrieving instances of anti-patterns defined for OntoUML. The effectiveness of these queries is assessed by applying them to a set of conceptual models of varying complexity that have been captured using gUFO.

Effective management of research data is crucial in modern scientific research, and ontologies and vocabularies play a significant role in describing and organizing such data. However, the abundance of available ontologies and vocabularies for various aspects of research data management (RDM) poses challenges in selecting the most suitable ones. This work aims to comprehensively analyze the key ontologies relevant to data stewardship and RDM. By investigating concepts, properties, interlinks, and potential overlaps, we establish and describe the relationships between these selected ontologies. Our analysis not only enhances understanding of existing ontologies and vocabularies used in RDM but also suggests practical applications for the outcomes of this study. For instance, we propose leveraging the findings to develop semantic data management plans in RDF, thereby improving the organization and accessibility of research data. Moreover, we identify potential ontologies for future extensions of this work.

Normalized Systems are a model-driven development technique focusing on evolvability and sustainability of resulting enterprise information systems. It applies well-defined principles to create a fine-grained modular structure while limiting combinatorial effects. The systems are generated (or, more precisely, expanded) from models of so-called NS Elements. NS has its own modelling language; however, interoperability with other knowledge representations is necessary. This work presents our solution for transforming knowledge kept in one of the most generic forms – RDF semantic triples – to NS Elements. The transformation enables mapping using RDFS/OWL ontologies describing both the input RDF. It utilizes SPARQL for executing the transformation to RDF based on the existing NS Element ontology. The presented transformation is demonstrated on an example case study, and its features and limitations are discussed together with potential future steps.

Normalized Systems (NS) theory describes how to design and develop evolvable systems. It is applied in practice to generate enterprise information systems using NS Expanders from models of NS Elements. As there are various well-established modelling languages, the possibility to (re-)use them to create NS applications is desired. This paper presents a mapping between the NS metamodel and the Ecore metamodel as a representant of essential structural modelling. The mapping is the basis of the transformation execution tool based on Eclipse Modeling Framework and NS Java libraries. Both the mapping and the tool are demonstrated in a concise case study but cover all essential Ecore constructs. During the work, several interesting similarities of the two metamodels were found and are described, e.g., its meta-circularity or ability to specify data types using references to Java classes. Still, there are significant differences between the metamodels that prevent some constructs from being mapped. The issues with information loss upon the transformation are mitigated by incorporating additional options that serve as key-value annotations. The results are ready to be used for any Ecore models to create an NS model that can be expanded into an NS application.