The terms ‘semantics’ and ‘ontology’ are increasingly appearing together with ‘explanation’, not only in the scientific literature, but also in everyday social interactions, in particular, within organizations. Ontologies have been shown to play a key role in supporting the semantic interoperability of data and knowledge representation structures used by information systems. With the proliferation of applications of Artificial Intelligence (AI) in different settings and the increasing need to guarantee their explainability (but also their interoperability) in critical contexts, the term ‘explanation’ has also become part of the scientific and technical jargon of modern information systems engineering. However, all of these terms are also significantly overloaded. In this paper, we address several interpretations of these notions, with an emphasis on their strong connection. Specifically, we discuss a notion of explanation termed ontological unpacking, which aims at explaining symbolic domain descriptions (e.g., conceptual models, knowledge graphs, logical specifications) by revealing their ontological commitment in terms of their so-called truthmakers, i.e., the entities in one’s ontology that are responsible for the truth of a description. To illustrate this methodology, we employ an ontological theory of relations to explain a symbolic model encoded in the de facto standard modeling language UML. We also discuss the essential role played by ontology-driven conceptual models (resulting from this form of explanation processes) in supporting semantic interoperability tasks. Furthermore, we revisit a proposal for quality criteria for explanations from philosophy of science to assess our approach. Finally, we discuss the relation between ontological unpacking and other forms of explanation in philosophy and science, as well as in the subarea of Artificial Intelligence known as Explainable AI (XAI).

This paper contributes to the philosophical foundations of conceptual modeling by addressing a number of foundational questions such as: What is a conceptual model? Among models used in computer science, which are conceptual, and which are not? How are conceptual models different from other models used in the Sciences and Engineering? The paper takes a stance in answering these questions and, in order to do that, it draws from a broad literature in philosophy, cognitive science, Logics, as well as several areas of Computer Science (including Databases, Software Engineering, Artificial Intelligence, Information Systems Engineering, among others). After a brief history of conceptual modeling, the paper addresses the aforementioned questions by proposing a characterization of conceptual models with respect to conceptual semantics and ontological commitments. Finally, we position our work w.r.t. to a "Reference Framework for Conceptual" modeling recently proposed in the literature.

It is widely recognized that accurately identifying and classifying competitors is a challenge for many companies and entrepreneurs. Nonetheless, it is a paramount activity which provide valuable insights that affect a wide range of strategic decisions. One of the main challenges in competitor identification lies in the complex nature of the competitive relationships that arise in business environments. These have been extensively investigate over the years, which lead to a plethora of competition theories and frameworks. Still, the concept of competition remains conceptually complex, as none of these approaches properly formalized their assumptions. In this paper, we address this issue by means of an ontological analysis on the notion of competition in general, and of business competition, in particular, leveraging theories from various fields, including Marketing, Strategic Management, Ecology, Psychology and Cognitive Sciences. Our analysis, the first of its kind in the literature, is grounded on the Unified Foundational Ontology (UFO) and allows us to formally characterize why competition arises, as well as to distinguish between three types of business competitive relationships, namely market-level, firm-level and potential competition.

Cities can be seen as systems of urban artefacts interacting with human activities. Since cities in this sense need to be organized and coordinated, convergences and divergences between the "planned" and the "lived" city have always been of paramount interest in urban planning. The increasing amount of geo big data and the growing impact of Internet of Things (IoT) in contemporary smart city is pushing toward a re-conceptualization of urban systems taking into consideration the complexity of human behaviors. This work contributes to this view by proposing an ontological analysis of urban artefacts and their roles, focusing in particular on the difference between social roles and functional roles through the prism of social practices.

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 analysis of value, value ascription mechanisms, and their motivating and influencing factors is useful for decision making processes of both customers and providers. In particular, enterprise modeling can greatly benefit from a proper understanding of value-related notions. Nonetheless, existing value models mainly focus on the value exchange between subjects, disregarding the understanding of “what” value is and of “why” something is valuable. In order to exploit the benefits of value analysis, a precise and rigorous conceptualization, based on foundational ontologies, is needed. To this aim, we present and discuss here a preliminary core ontology of value ascription and we discuss the main issues.

The requirements for most software systems - the intended states-of-affairs these systems are supposed to bring about - concern their operational environment, often a socio-physical world. But software systems usually don't have any direct means to change that environment in order to bring about the intended states-of-affairs. In what sense then can we say that such systems fulfill their requirements? The main purpose of this paper is to account for this paradox. We do so by proposing a preliminary Ontology of Assumptions. This ontology aims to characterize and make explicit a number of notions that are used implicitly in software engineering practice to establish that a system specification S fulfills its requirements R given a set of assumptions A. Our proposal is illustrated with an example concerning a meeting scheduler.

Although software plays an essential role in modern society, its ontological nature is still unclear. For many, software is just code, but this is not illuminating. Several researchers have attempted to understand the core nature of software and programs in terms of concepts such as code, copy, medium and execution. More recently, a proposal was made to consider software as an abstract artifact, distinct from code, just because code may change while the software remains the same. We explore in this paper the implications of such a proposal in the light of software engineering and requirements engineering literature. We make a sharp distinction between different kinds of software artifacts (code, program, software system, and software product), and describe the ways they are inter-connected in the context of a software engineering process.

The concept of "service" has been characterized by different disciplines and authors from various points of view. The variety of characterizations reveals that this notion, although an intuitive one, is far from trivial. Given the importance of services in enterprise computing and Service Science in general, we believe that a clear account of services and service-related concepts is necessary and would serve as a basis for communication, consensus and alignment of various approaches and perspectives. In this paper we propose a commitment-based account of the notion of service captured in a core reference ontology called UFO-S. We address the commitments established between service providers and customers, and show how such commitments affect the service lifecycle. We show that the commitment-based account can serve to harmonize different notions of service in the literature.

In this paper we present an ontological framework for modeling the core concepts of personal income taxes, based on the Italian law. The ontological analysis focuses on an high-level conceptualization of the main principles of tax legislation, and is largely based on contributions from legal doctrine and the most relevant Italian Constitutional Court's decisions in tax law. As such, the model may serve as a framework to be specialized by further ontological modules. In addition to the core ontological concepts of tax domain, an emphasis is given to the norms application process, which we believe helps to explain the complicated way taxes are imposed. In our approach, the final result of this process is a tax position, which accounts for the relationship between the taxpayer and the treasury with respect to a particular tribute.

In this paper we propose an analysis and an upgrade of WordNet's top-level synset taxonomy. We briefly review WordNet and identify its main semantic limitations. Some principles from a forthcoming OntoClean methodology are applied to the ontological analysis of WordNet. A revised top-level taxonomy is proposed, which is meant to be more conceptually rigorous, cognitively transparent, and efficiently exploitable in several applications.