Tamquam alter idem: formal similarities in a subset of reports on anti-inflammatory compounds in the years 2008–2019

Scientometrics - A literature search on the in vitro testing of anti-inflammatory compounds of natural origin revealed a considerable number of studies adopting a similar template for data...     

Automated conceptual model clustering: a relator-centric approach

Software and Systems Modeling - In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains...     

An ontology-based analysis and semantics for organizational structure modeling in the ARIS method

This paper focuses on the issue of ontological analysis for organizational structure modeling in the ARIS method with the following contributions: (i) an interpretation of the language in terms of real-world entities in the UFO foundational ontology; (ii) the identification of inappropriate elements of the language, using a systematic ontology-based analysis approach; and (iii) recommendations for improvements of the language to resolve the issues identified.     

Transforming OntoUML into Alloy: towards conceptual model validation using a lightweight formal method

While conceptual modeling is strongly related to the final quality of the software product, conceptual modeling itself remains a challenging activity. In particular, modelers must ensure that conceptual models properly formalize their intended conceptualization of a domain. This paper proposes an approach to facilitate the validation process of conceptual models defined in OntoUML by transforming these models into specifications in the logic-based language Alloy and using its analyzer to generate instances of the model and assertion counter-examples. By allowing the observation of sequences of snapshots of model instances, the dynamics of object creation, classification, association and destruction are revealed. This confronts the modeler with the implications of modeling choices and allows them to uncover mistakes or gain confidence in the quality of conceptual models.