Alessander Botti Benevides

A Model-Based Tool for Conceptual Modeling and Domain Ontology Engineering in OntoUML

This paper presents a Model-Based graphical editor for supporting the creation of conceptual models and domain ontologies in a philosophically and cognitively well-founded modeling language named OntoUML. The Editor is designed in a way that, on one hand, it shields the user from the complexity of the ontological principles underlying this language. On the other hand, it reinforces these principles in the produced models by providing a mechanism for automatic formal constraint verification.

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.

Assessing Modal Aspects of OntoUML Conceptual Models in Alloy

Assessing the quality of conceptual models is key to ensure that conceptual models can be used effectively as a basis for understanding, agreement and construction of information systems. This paper proposes an approach to assess conceptual models defined in OntoUML by transforming these models into specifications in the logic-based language Alloy. These Alloy specifications include the modal axioms of the theory underlying OntoUML, allowing us to validate the modal meta-properties of the OntoUML types and part-whole relations.

A SSVEP-BCI Setup Based on Depth-of-Field

In optical systems, the range of distance near the point of focus where objects are perceived sharply is referred as depth-of-field; objects outside this region are defocused and blurred. Furthermore, ophthalmology studies state that the amplitude and the latency of visual evoked potentials are affected by defocusing. In this context, this paper evaluates a novel setup for a steady-state visual evoked potential (SSVEP) brain–computer interface, in which two stimuli are presented together in the center of the user’s field of view but at different distances ensuring that if one stimulus is focused on, the other one is non-focused, and vice versa. The evaluationwas conductedwith eight healthy subjects who were asked to focus on just one stimulus at a time. An average accuracy rate of 0.93 was achieved for a time window of 4 s by employing well know SSVEP detection methods. Results show that distinguishable SSVEP can be elicited by the focused stimulus regardless of the non-focused one is also present in the field of view. Finally, this approach allows users to send commands through a stimuli selection by focusing mechanism that does not demand neck, head, and/or eyeball movements.

Endurant Types in Ontology-Driven Conceptual Modeling: Towards OntoUML 2.0

For over a decade now, a community of researchers has contributed to the development of the Unified Foundational Ontology (UFO) - aimed at providing foundations for all major conceptual modeling constructs. This ontology has led to the development of an Ontology-Driven Conceptual Modeling language dubbed OntoUML, reflecting the ontological micro-theories comprising UFO. Over the years, UFO and OntoUML have been successfully employed in a number of academic, industrial and governmental settings to create conceptual models in a variety of different domains. These experiences have pointed out to opportunities of improvement not only to the language itself but also to its underlying theory. In this paper, we take the first step in that direction by revising the theory of types in UFO in response to empirical evidence. The new version of this theory shows that many of the meta-types present in OntoUML (differentiating Kinds, Roles, Phases, Mixins, etc.) should be considered not as restricted to Substantial types but instead should be applied to model Endurant Types in general, including Relator types, Quality types and Mode types. We also contribute a formal characterization of this fragment of the theory, which is then used to advance a metamodel for OntoUML 2.0. Finally, we propose a computational support tool implementing this updated metamodel.

The interplay between models and observations

We propose a formal framework to examine the relationship between models and observations. To make our analysis precise, models are reduced to first-order theories that represent both terminological knowledge—e.g., the laws that are supposed to regulate the domain under analysis and that allow for explanations, predictions, and simulations—and assertional knowledge—e.g., information about specific entities in the domain of interest. Observations are introduced into the domain of quantification of a distinct first-order theory that describes their nature and their organization and takes track of the way they are experimentally acquired or intentionally elaborated. A model mainly represents the theoretical knowledge or hypotheses on a domain, while the theory of observations mainly represents the empirical knowledge and the given experimental practices. We propose a precise identity criterion for observations and we explore different links between models and observations by assuming a degree of independence between them. By exploiting some techniques developed in the field of social choice theory and judgment aggregation, we sketch some strategies to solve inconsistencies between a given set of observations and the assumed theoretical hypotheses. The solutions of these inconsistencies can impact both the observations—e.g., the theoretical knowledge and the analysis of the way observations are collected or produced may highlight some unreliable sources—and the models—e.g., empirical evidences may invalidate some theoretical laws.