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GUM is a linguistically-motivated ontology originally developed to support natural language processing systems by offering a level of representation intermediate between linguistic forms and domain knowledge. Whereas modeling decisions for individual domains may need to be responsive to domain-specific criteria, a linguistically-motivated ontology offers a characterization that generalizes across domains because its design criteria are derived independently both of domain and of application. With respect to this mediating role, the use of GUM resembles (and partially predates) the adoption of upper ontologies as tools for mediating across domains and for supporting domain modeling. This paper briefly introduces the ontology, setting out its origins, design principles and applications. The example cases for this special issue are then described, illustrating particularly some of the principal differences and similarities of GUM to non-linguistically motivated upper ontologies. [ABSTRACT FROM AUTHOR]

dolce, the first top-level (foundational) ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. dolce is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. dolce clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet). Being a foundational ontology, dolce is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years dolce has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases. [ABSTRACT FROM AUTHOR]

Upper ontologies have traditionally arisen from the approach in which concepts that are common across a set of domains can be axiomatized at a general level. The rationale is that reuse across domains is to be supported through specialization of the general concepts from an upper ontology. Similarly, semantic integration between ontologies is to be achieved through the general concepts they specialize. The TUpper Ontology follows an alternative approach (referred to as the sideways approach) to the conventional upper ontology paradigm. Rather than think of an upper ontology as a monolithic axiomatization centred on a taxonomy, the sideways approach considers an upper ontology to be a modular ontology composed of generic ontologies that cover concepts including those related to time, process, and space. TUpper is therefore composed of a set of generic ontologies, and each generic ontology axiomatizes a particular set of generic concepts (e.g., the classes and relations relevant for time, process, and space). The TUpper Ontology is designed as a top-level ontology that contains modules from the ontologies within existing international standards. [ABSTRACT FROM AUTHOR]

dolce, the first top-level (foundational) ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. dolce is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. dolce clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet). Being a foundational ontology, dolce is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years dolce has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases. [ABSTRACT FROM AUTHOR]

Basic Formal Ontology (BFO) is a top-level ontology consisting of thirty-six classes, designed to support information integration, retrieval, and analysis across all domains of scientific investigation, presently employed in over 350 ontology projects around the world. BFO is a genuine top-level ontology, containing no terms particular to material domains, such as physics, medicine, or psychology. In this paper, we demonstrate how a series of cases illustrating common types of change may be represented by universals, defined classes, and relations employing the BFO framework. We provide discussion of these cases to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using different top-level ontologies. [ABSTRACT FROM AUTHOR]

The General Formal Ontology (GFO) is a top-level ontology that is being developed at the University of Leipzig since 1999. Besides introducing some of the basic principles of the ontology, we expound axiomatic fragments of its formalization and present ontological models of several use cases. GFO is a top-level ontology that integrates objects and processes into a unified framework, in a way that differs significantly from other ontologies. Another unique selling feature of GFO is its meta-ontological architecture, which includes set theory into ontology and which accounts for its specific role in common representation approaches. The second level of that architecture starts from the distinction of categories and individuals, which forms the backbone of the world's structure. Furthermore, GFO comprises several kinds of categories, among them universals and concepts, and it considers several ontological regions and levels. In the context of this special issue paper, we study five pre-determined use cases from the perspective of GFO. The results of these analyses yield insights into how the ontology treats several important notions. Very abridged, this covers material objects and their composition; roles and social entities; properties with their relations to objects and processes, and their changing; changes of processes, including a functional perspective; and, eventually, the nature and changing of concepts as well as terminology. A final part summarizes application projects that use GFO in various contexts. [ABSTRACT FROM AUTHOR]

The Unified Foundational Ontology (UFO) was developed over the last two decades by consistently putting together theories from areas such as formal ontology in philosophy, cognitive science, linguistics, and philosophical logics. It comprises a number of micro-theories addressing fundamental conceptual modeling notions, including entity types and relationship types. The aim of this paper is to summarize the current state of UFO, presenting a formalization of the ontology, along with the analysis of a number of cases to illustrate the application of UFO and facilitate its comparison with other foundational ontologies in this special issue. (The cases originate from the First FOUST Workshop – the Foundational Stance, an international forum dedicated to Foundational Ontology research.) [ABSTRACT FROM AUTHOR]

Geometric data models form the backbone of virtually all spatial information systems, such as GIS, CAD, and CAM. Yet a lot of spatial information from textual sources, including historical documents or social media, is predominantly of qualitative, especially mereotopological, rather than geometric-quantitative nature. While mereotopological theories have been extensively studied in Logic, Computer Science, Cognitive Science, and Geographic Information Science, most are unidimensional mereotopologies in the sense that only entities of a single dimension are permitted to co-exist. Integrating mereotopological information with geometric data requires a multidimensional mereotopology, which permits entities of different dimensions to co-exist, similarly to how geometric and algebraic topological data models permit points, simple lines, polylines, cells, polygons, and polyhedra to co-exist. It further requires complex spatial objects to be represented as sets of atomic entities such that spatial relations between complex objects can be computed from the relations of the atomic entities in their decomposition. This paper provides a comprehensive study of CODI, a first-order logic ontology of multidimensional mereotopology. An axiomatization of mereological closure operations of intersection, difference, and sums for CODI is proposed in which these operations apply to all pairs of spatial entities regardless of their dimension. It is proved that for atomic models – and thus all finite models – the extended theory is indeed able to decompose all spatial entities into a partition of atomic parts. A full representation of the models as sets of Boolean algebras verifies this. The closure operations are further shown to satisfy important mereological principles from unidimensional mereotopology and to preserve many of the mathematical properties of set intersection and set difference. [ABSTRACT FROM AUTHOR]

Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) technologies that offer opportunities to improve abnormal situation detection when integrating medical wearables and cloud infrastructure. Usually, these systems rely on standardised IoT ontologies to represent sensor data observations. The ETSI Smart Applications REFerence ontology (SAREF) is an extensible industry-oriented standard. In this paper, we explain the need for interoperability of IoT healthcare applications and the role of standardised ontologies to achieve semantic interoperability. In particular, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), emphasizing the requirement of representing time series. We compared the main ontologies in this context, according to quality, message size (payload), IoT-orientation and standardisation. Here we describe the first attempt to extend SAREF for specific e-Health use cases related to ECG data, the SAREF4health extension, which tackles the verbosity problem. Ontology-driven conceptual modelling was applied to develop SAREF4health, in which an ECG ontology grounded in the Unified Foundational Ontology (UFO), which plays the role of a reference model. The methodology was enhanced by following a standardisation procedure and considering the RDF implementation of the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. The validation of SAREF4health includes the responses to competency questions, as well as the development and tests of an IoT Early Warning System prototype that uses ECG data and collision identification to detect accidents with truck drivers in a port area. This prototype integrates an existing ECG wearable with a cloud infrastructure, demonstrating the performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health enables the semantic interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off between ontology quality and aggregation representation are also discussed. [ABSTRACT FROM AUTHOR]

Part–whole relations are pervasive throughout domain ontologies and enjoy interest also in, inter alia, NLP and manufacturing, and by philosophers in the scope of mereology. There exist a stable list of part–whole relations that are assumed to be common, yet for isiZulu, among other languages, there were at least linguistic differences. This raises the question whether there are ontological differences, which would imply that the 'common' list is not universal across languages and cultures. We investigated this for 18 part–whole terms in the Zulu language that we selected from an initial list of 81 terms collected. They were formalised and aligned to the well-known part–whole relations, and checked against a corpus. While there is a term for general parthood in Zulu, the main difference observed concerns relation proliferation due to very specific relata that are entities typically represented only in domain ontologies. This poses new questions for ontology engineering on how to manage the plurality of relations and for philosophy to possibly extend mereology. [ABSTRACT FROM AUTHOR]

Quantities and units of measure provide an important means by which intelligent agents interact with the physical world. Although multiple ontologies for quantities and units of measure have been proposed within the Applied Ontology community, they often incorporate questionable ontological commitments. Quantities are combined using notions of dimensional analysis that often conflate the combination of units with algebraic operations on real numbers. In this paper, we present an alternative approach that shifts the focus to the connection between kinds of measurements associated with a unit and the physical objects and processes that are being measured. One of the key features of this approach is that it makes minimal ontological commitments with respect to the TUpper upper ontology – the only new classes that are introduced are the classes for kinds of measures and associated units. We propose axiomatizations of the intended semantics for combining measurements, and the correct axiomatization of the relationship between the quantities and units of measure and the existing upper ontology. [ABSTRACT FROM AUTHOR]

When you meet a delivery robot in a narrow street it stops to let you pass. It was built to give you precedence. What happens if you run into a robot that was not trained by or for humans? The existence in our environment of robots which do not abide by human behavioral rules and social systems might sound odd, but is a case we may encounter in the future. In this paper, self-taught robots are artificial embodied agents that, thanks for instance to AI learning techniques, manage to survive in the environment without embracing behavioral or judgment rules given and used by humans. The paper argues that our ontological systems are not suitable to understand and cope with artificial agents. The arguments are speculative rather than empirical, and the goal is to drive attention to new ontological challenges. [ABSTRACT FROM AUTHOR]