Tiago Prince Sales

Detection, Simulation and Elimination of Semantic Anti-patterns in Ontology-Driven Conceptual Models

The construction of large-scale reference conceptual models is a complex engineering activity. To develop high-quality models, a modeler must have the support of expressive engineering tools such as theoretically well-founded modeling languages and methodologies, patterns and anti-patterns and automated support environments. This paper proposes Semantic Anti-Patterns for ontology-driven conceptual modeling. These anti-patterns capture error prone modeling decisions that can result in the creation of models that allow for unintended model instances (representing undesired state of affairs). The anti-patterns presented here have been empirically elicited through an approach of conceptual models validation via visual simulation. The paper also presents a tool that is able to: automatically identify these anti-patterns in user’s models, provide visualization for its consequences, and generate corrections to these models by the automatic inclusion of OCL constraints.

OntoUML Lightweight Editor: A Model-Based Environment to Build, Evaluate and Implement Reference Ontologies

Enterprise information systems are increasingly being conceived as a combination of existing systems and to work as a part of an ecosystem of software products. This change demands methods and tools to deal with the challenging semantic interoperability issues. OntoUML is a well-founded modeling language that allows modelers to formalize world-views in a technologically neutral way, aiding in the solution of such interoperability challenges. In this paper, we present an overview of the OntoUML Lightweight Editor (OLED), our model-based environment to build, evaluate and implement OntoUML models, alongside with its main features and application scenarios.

Ontological anti-patterns

The construction of large-scale reference conceptual models is a complex engineering activity. To develop high-quality models, a modeler must have the support of expressive engineering tools such as theoretically well-founded modeling languages and methodologies, patterns and anti-patterns and automated supporting environments. This paper proposes a set of Ontological Anti-Patterns for Ontology-Driven Conceptual Modeling. These anti-patterns capture error-prone modeling decisions that can result in the creation of models that fail to exclude unintended model instances (representing unintended state of affairs) or forbid intended ones (representing intended states of affairs). The anti-patterns presented here have been empirically elicited through an approach of conceptual models validation via visual simulation. The paper also presents a series of refactoring plans for rectifying the models in which these anti-patterns occur. In addition, we present here a computational tool that is able to: automatically identify these anti-patterns in users models, guide users in assessing their consequences, and generate corrections to these models by the automatic inclusion of OCL constraints implementing the proposed refactoring plans. Finally, the paper also presents an empirical study for assessing the harmfulness of each of the uncovered anti-patterns (i.e., the likelihood that its occurrence in a model entails unintended consequences) as well as the effectiveness of the proposed refactoring plans.

An Ontological Analysis of Value Propositions

In competitive markets, companies need well-designed business strategies if they seek to grow and obtain sustainable competitive advantage. At the core of a successful business strategy there is a carefully crafted value proposition, which ultimately defines what a company delivers to its customers. Despite their widely recognized importance, there is however little agreement on what exactly value propositions are. This lack of conceptual clarity harms the communication among stakeholders and the harmonization of current business strategy theories and strategy support frameworks. Furthermore, it hinders the development of systematic methodologies for crafting value propositions, as well as adequate support for representing and analyzing them. In this paper, we present an ontological analysis of value propositions based on a review of most relevant business and marketing theories and on previous work on value ascription, grounded in the Unified Foundational Ontology (UFO). Our investigation clarifies how value propositions are different from value presentations, and shows the difference between value propositions at the business level from those related to specific offerings.

The Common Ontology of Value and Risk.

Risk analysis is traditionally accepted as a complex and critical activity in various contexts, such as strategic planning and software development. Given its complexity, several modeling approaches have been proposed to help analysts in representing and analyzing risks. Naturally, having a clear understanding of the nature of risk is fundamental for such an activity. Yet, risk is still a heavily overloaded and conceptually unclear notion, despite the wide number of efforts to properly characterize it, including a series of international standards. In this paper, we address this issue by means of an in-depth ontological analysis of the notion of risk. In particular, this analysis shows a surprising and important result, namely, that the notion of risk is irreducibly intertwined with the notion of value and, more specifically, that risk assessment is a particular case of value ascription. As a result, we propose a concrete artifact, namely, the Common Ontology of Value and Risk, which we employ to harmonize different conceptions of risk existing in the literature.

Reification and Truthmaking Patterns

Reification is a standard technique in conceptual modeling, which consists of including in the domain of discourse entities that may otherwise be hidden or implicit. However, deciding what should be reified is not always easy. Recent work on formal ontology offers us a simple answer: put in the domain of discourse those entities that are responsible for the (alleged) truth of our propositions. These are called truthmakers. Re-visiting previous work, we propose in this paper a systematic analysis of truthmaking patterns for properties and relations based on the ontological nature of their truthmakers. Truthmaking patterns will be presented as generalization of reification patterns, accounting for the fact that, in some cases, we do not reify a property or a relationship directly, but we rather reify its truthmakers.

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.

“Is It a Fleet or a Collection of Ships?”: Ontological Anti-patterns in the Modeling of Part-Whole Relations

Over the years, there is a growing interest in employing theories from philosophical ontology, cognitive science and linguistics to devise theoretical, methodological and computational tools for information systems engineering, in general, and for conceptual modeling, in particular. In this paper, we discuss one particular kind of such tools, namely, ontological anti-patterns. Ontological anti-patterns are error-problem modeling structures that can create a deviation between the possible and the intended interpretations of a model. In this paper, we present two empirically elicited ontological anti-patterns related to the modeling of part-whole relations. In particular, these anti-patterns identify possible mistakes in the modeling of collectives (complex entities that have a uniform role-based structure) and functional complexes (complex entities composed of functional parts). Besides identifying these anti-patterns, the paper presents a series of rectification plans that can be used to eliminate their occurrence in models. Finally, we present a model-based computational tool that supports the automated detection, analysis and elimination of these anti-patterns.