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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.

In the domain of the platform economy we identified two gaps in the current literature. First the lack of a shared conceptualization of digital platform types. We solve this issue with a taxonomy giving an overview of digital platform attributes, with attribute values expressing the possible variations between platforms depending on their type. These attributes and attribute values are then used in the typology giving a clear overview of all the digital platform types and how they are related to each other. The second gap is the lack of knowledge concerning the software functionalities of digital platforms. We contribute to this with a proposal of a reference ontology allowing for a better communication between developers and other stakeholders. Our envisioned ontology can be used as a common language that all platform stakeholders can understand to facilitate future research and support ontology-driven development of digital platforms.

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.

Recently, ontology-based approaches to security, in particular to information security, have been recognized as a relevant challenge and as an area of research interest of its own. As the number of ontologies about security grows for supporting different applications, semantic interoperability issues emerge. Relatively little attention has been paid to the ontological analysis of the concept of security understood as a broad application-independent security ontology. Core (or reference) ontologies of security cover this issue to some extent, enabling multiple applications crossing domains of security (information systems, economics, public health, crime etc.). In this paper, we investigate the current state-of-the-art on Security Core Ontologies. We select, analyze, and categorize studies on this topic, supporting a future ontological analysis of security, which could ground a well-founded security core ontology. Notably, we show that: most existing ontologies are not publicly findable/accessible; foundational ontologies are under-explored in this field of research; there seems to be no common ontology of security. From these findings, we make the case for the need of a FAIR Core Security Ontology.

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.

Part 2:Enterprise Modeling and Enterprise Architecture; International audience; Trust is widely acknowledged as the cornerstone of relationships in social life. But what makes an agent trust a person, a resource or an organization? Which characteristics should a trustee have in order to be considered trustworthy? The importance of understanding trust in organizations has motivated us to investigate the representation of trust concerns in enterprise models. Based on a well-founded reference ontology of trust, we propose a pattern language for trust modeling in ArchiMate. We present a first iteration of the design cycle, which includes the development of the pattern language and its demonstration by means of a realistic case study about trust in a COVID-19 data repository.

In recent years, there has been an increasing interest in the development of well-founded conceptual models for Service Management, Accounting Information Systems and Financial Reporting. Economic exchanges are a central notion in these areas and they occupy a prominent position in frameworks such as the Resource-Event Action (REA) ISO Standard, service core ontologies (e.g., UFO-S) as well as financial standards (e.g. OMG’s Financial Industry Business Ontology - FIBO). We present a core ontology for economic exchanges inspired by a recent view on this phenomenon. According to this view, economic exchanges are based on an agreement on the actions that the agents are committed to perform. This view enables a unified treatment of economic exchanges, regardless the object of the transaction. We ground our core ontology on the Unified Foundational Ontology (UFO), discussing its formal and conceptual aspects, instantiating it as a reusable OntoUML model, and confronting it with the REA standard and the UFO-S service ontology.

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.

Digital platforms can be categorized into different types including ‘multi-sided platform’, ‘digital marketplace’, ‘crowdfunding platform’, ‘sharing economy platform’ and ‘on-demand platform’. As there is a lack of knowledge regarding the requirements and design of these digital platform types, we developed a method to design a digital platform reference ontology based on a taxonomy. The taxonomy provides an overview of digital platform properties, with the property values expressing the possible variations between digital platforms depending on their type. For each property value, we can create a digital platform reference ontology module using the five-step approach proposed by Ruy et al. [1] based on the patterns of the Unified Foundational Ontology (UFO). UFO is a high-level ontology that provides us with basic concepts for objects, events, social elements and their types, relations and properties. These digital platform reference ontology modules can be combined as building blocks to compose our reference ontology for expressing the functionality for digital platforms of all types. We believe this reference ontology can be a step towards a better understanding of digital platform functionality, better communication between stakeholders and eventually may facilitate future research and development of digital platforms.

Part 5:Enterprise Ontologies; International audience; In recent years, there has been a growing interest, within the financial sector, in the adoption of ontology-based conceptual models to make the nature of conceptualizations explicit, as well as to safely establish the correct relations between them, thereby supporting semantic interoperability. Despite the wide number of efforts to create a unified view of the reality related to economic and financial domains, no comprehensive enough formal model has been developed to, on one hand, accurately describe the semantics regarding the world of money and currencies and, on the other hand, differentiate them from virtual currencies - of which cryptocurrencies are the most significant representative. This research aims at tackling these questions by conducting an ontological analysis of money and related concepts, grounded in the Unified Foundational Ontology, based on a literature review of the most relevant economic theories, and considering recent innovations in the financial industry.

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).

Recently the sharing economy has emerged as a viable alternative to fulfilling a variety of consumer needs. As there is no consensus on the definition of ‘sharing economy’ we use the term ‘marketplace’ to refer more specifically to Internet/software-based sharing economy platforms connecting two different market segments. In the field of sharing economy and marketplaces we found a research gap concerning the (socio)technological aspects and the development of marketplaces. A marketplace ontology can help to have a clear account of marketplace concepts which will facilitate communication, consensus and alignment. In this paper we design this marketplace ontology in four steps. First the selection of UFO as foundation and UFO-S as core ontology. Second the search for a set of minimal conditions and properties common for marketplaces and the derivation into competency questions. Third, use the competency questions to identify fragmented sub-ontology pieces called Domain-Related Ontology Patterns (DROPs) and apply them informally by extending UFO-S concepts to design a marketplace domain ontology. This marketplace domain ontology is represented in OntoUML. The last step is the validation of the OntoUML model using expert knowledge.

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.

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 user's 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.

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.

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.

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.

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.

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.

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.

Instance-level modelling is a sort of conceptual modelling that deals with concrete objects instead of general classes and types. Instance-level modelling approach offers a rather innovative way for communication with domain experts extremely useful for them, as they can see their real data in the context of the given model. Various approaches were presented in the paper “Instance-Level modelling and Simulation Using Lambda-Calculus and Object-Oriented Environments” at EOMAS 2011. The present paper is a sequel and it presents additional approaches we find useful in practice: Fact-oriented modelling, OntoUML in combination with OCL and the Alloy and Eclipse-based framework DresdenOCL. We present key features of the various approaches and demonstrate them on a running example, we follow up with a discussion comparing these approaches. Notice that OntoUML combined with the Alloy is an original research achievement built on the research of OntoUML.

OntoUML is a promising method for ontological modelling. In this paper, we discuss its possible use for software engineering. We propose a method of transformation of an ontological model into a software-engineering object-oriented class model in UML and its instantiation. Our approach is based on the following best practices: pure object-oriented paradigm and approach of dividing state and identity as introduced in the Clojure programming language.