Search

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 (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, domain experts must be able to understand and reason with their content. In other words, these models need to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages on the rich semantics of ontology-driven conceptual models (ODCM). In particular, we propose a formal notion of Relational Context to guide the automated clusterization (or modular breakdown) of conceptual models. Such Relational Contexts capture all the information needed for understanding entities "qua players of roles" in the scope of an objectified (reified) relationship (relator). The paper also presents computational support for automating the identification of Relational Contexts and this modular breakdown procedure. Finally, we report the results of an empirical study assessing the cognitive effectiveness of this approach.

Conceptual modeling plays a fundamental role to capture information about complex business domains (e.g., finance, healthcare) and enables semantic interoperability. To fulfill their role, conceptual models must contain the exact set of constraints that represent the worldview of the relevant domain stakeholders. However, as empirical results show, modelers are subject to cognitive limitations and biases and, hence, in practice, they produce models that fall short in that respect. Moreover, the process of formally designing conceptual models is notoriously hard and requires expertise that modelers do not always have. This paper falls in the general area concerned with the development of artificial intelligence techniques for the enterprise. In particular, we propose an approach that leverages model finding and inductive logic programming (ILP) techniques. We aim to move towards supporting modelers in identifying domain constraints that are missing from their models, and thus improving their precision w.r.t. their intended worldviews. Firstly, we describe how to use the results produced by the application of model finding as input to an inductive learning process. Secondly, we test the approach with the goal of demonstrating its feasibility and illustrating some key design issues to be considered while using these techniques.

Types are fundamental for conceptual modeling and knowledge representation, being an essential construct in all major modeling languages in these fields. Despite that, from an ontological and cognitive point of view, there has been a lack of theoretical support for precisely defining a consensual view on types. As a consequence, there has been a lack of precise methodological support for users when choosing the best way to model general terms representing types that appear in a domain, and for building sound taxonomic structures involving them. 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. At the core of this enterprise, there has been a theory of types specially designed to address these issues. This theory is ontologically well-founded, psychologically informed, and formally characterized. These results have led to the development of a Conceptual Modelling language dubbed OntoUML, reflecting the ontological micro-theories comprising UFO. Over the years, UFO and OntoUML have been successfully employed on conceptual model design in a variety of domains including academic, industrial, and governmental settings. These experiences exposed improvement opportunities for both the OntoUML language and its underlying theory, UFO. In this paper, we revise the theory of types in UFO in response to empirical evidence. The new version of this theory shows that many of OntoUML’s meta-types (e.g. kind, role, phase, mixin) 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 with a formal characterization of this fragment of the theory, which is then used to advance a new metamodel for OntoUML (termed OntoUML 2). To demonstrate that the benefits of this approach are extended beyond OntoUML, the proposed formal theory is then employed to support the definition of UFO-based lightweight Semantic Web ontologies with ontological constraint checking in OWL. Additionally, we report on empirical evidence from the literature, mainly from cognitive psychology but also from linguistics, supporting some of the key claims made by this theory. Finally, we propose a computational support for this updated metamodel.

Validating and debugging conceptual models is a very time-consuming task. Though separate software tools for model validation and machine learning are available, their integration for an automated support of the debugging-validation process still needs to be explored. The synergy between model validation for finding intended/unintended conceptual models instances and machine learning for suggesting repairs promises to be a fruitful relationship. This paper provides a preliminary description of a framework for an adequate automatic support to engineers and domain experts in the proper design of a conceptual model. By means of a running example, the analysis will focus on two main aspects: i) the process by which formal, tool-supported methods can be effectively used to generate negative and positive examples, given an input conceptual model; ii) the key role of a learning system in uncovering error-prone structures and suggesting conceptual modeling repairs.

Part 5:Enterprise Ontologies; International audience; 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 (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, it is essential that domain experts are able to understand and reason with their content. In other words, it is important for these reference conceptual models to be cognitively tractable. This paper contributes to this goal by proposing a model clustering technique that leverages the rich semantics of ontology-driven conceptual models (ODCM). In particular, the technique employs the notion of Relational Context to guide automated model breakdown. Such Relational Contexts capture all the information needed for understanding entities “qua players of roles” in the scope of an objectified (reified) relationship (relator).

Trust is a key component of relationships in social life. It is commonly argued that trust is the \glue" that holds families, societies, organizations and companies together. In the literature trust is frequently considered as a strategic asset for organizations. Having a clear understanding of the notion of trust and its components is paramount to both trust assessment and trust management. Although much progress has been made to clarify the ontological nature of trust, the term remains overloaded and there is not yet a shared or prevailing, and conceptually clear notion of trust. In this paper we address this issue by means of an in-depth ontological analysis of the notion of trust, grounded in the Unified Foundational Ontology. As a result, we propose a concrete artifact, namely, the Reference Ontology for Trust, in which we characterize the general concept of trust and distinguish between two types of trust, namely, social trust and institution-based trust. We also represent the emergence of risk from trust relations. In addition, we make a comparative analysis of our Reference Ontology to other trust ontologies. To validate and demonstrate the contribution of our approach, we apply it to model two application examples.

Recently, we witnessed a shift in the Networking paradigm, with large part of the network control moving from hardware to software. This move has been accompanied by an increase of interest in declarative software models (conceptual models) for the domain. Moreover, novel architectures allow services to be deployed in multiple domains. These changes call for new business models to allow the commercialization of Virtual Network Functions (VNFs). This paper proposes the creation of an ontology-based reference conceptual model to support VNF Marketplaces, allowing VNF vendors and infrastructure providers to commercialize VNFaaS (VNFs as services). The proposed reference model has been engineered by using foundational ontology techniques (UFO/OntoUML), it has been formally validated by using model simulation techniques, and it has been implemented in OWL.

Risk analysis is a complex and critical activity in various contexts, ranging from strategic planning to IT systems operation. Given its complexity, several Enterprise Architecture (EA) frameworks and modeling languages have been developed to help analysts in representing and analyzing risks. Yet, the notion of risk remains overloaded and conceptually unclear in most of them. In this paper, we investigate the real-world semantics underlying risk-related constructs in one of such approaches, namely ArchiMate's Risk and Security Overlay (RSO). We perform this investigation by means of ontological analysis to reveal semantic limitations in the overlay, such as ambiguity and missing constructs. Building on the results of this analysis, we propose a well-founded redesign of the risk modeling aspects of the RSO.

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.