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 Yet a lot of spatial information from textual sources, including historical documents or social media, is predominantly of qualitative, especially mereotopological, rather than geometric -quantitative nature.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.

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.

In this paper, we present a domain-specific, open access, reference ontology (ROMAIN) for the maintenance management domain. We use a hybrid approach, based on a top-down alignment to an open source top-level ontology, the Basic Formal Ontology (BFO), and a bottom up focus on classes that are groun ded in maintenance practice. We constrain the scope of the ontology to the classes that are unique to the maintenance management practice, such as maintenance strategy, degradation, and work order management, rather than modeling the entire domain of maintenance. This approach reduces the scope of the development task and enables reasoning to be tested at a manageable scale. ROMAIN provides a unifying framework that can be used in conjunction with other BFO compliant sub-domain ontologies, such as planning and scheduling ontologies. The proposed ontology is validated using real-life data in the context of a use case related to evaluating the effectiveness of maintenance strategy.

Decision making is an important part of human activities, and is a very active area of research. In this article, a formal framework for decision making is developed. At the heart of the framework is an ontology for the decision making process that classifies both the kinds of data that are availab le for the decision making process and the modes of reasoning that are used to develop situation awareness and to make decisions. This ontology-driven framework resolves two outstanding problems in decision making systems: providing a theoretical foundation and declarative language support. Concerns are addressed such as providing for multiple notions of time, recording the activities used in decision making, and maintaining the provenance of both data and activities. A process model is developed that allows for flexible execution of the activities, including either synchronous or asynchronous processing and for exploring alternative or concurrent branches at any stage of the process. Detailed implementation techniques are presented. An issue tracking system is used as a running example to illustrate the concepts being developed. Use cases for the framework are given in a variety of domains including customer support, healthcare, cloud services, and the Internet of Things.

The National Cancer Institute thesaurus is an important knowledge resource that should ideally be error-free. We investigated the occurrence of errors in the Neoplasm subhierarchy, which is a part of the National Cancer Institute thesaurus Disease, Disorder or Finding hierarchy. There are five key findings in this study. (1) Errors in the Neoplasm subhierarchy are not uniformly distributed. (2) A partial-area taxonomy, which is a compact network for summarizing the structure and content of an ontology, helped uncover groups of concepts, called “small partial-areas,” in the Neoplasm subhierarchy. (3) The rate of errors in “small partial-areas” is twice as large as in “large partial-areas” (44% versus 22%), satisfying statistical significance. Thus, we conclude that higher error concentrations exist in small partial-areas. (4) Group-based auditing can be used successfully to identify additional suspicious concepts in a small group, once a few members of the group are already known as erroneous. (5) Error correction propagation can be used successfully and with minimal effort to correct additional errors in the Neoplasm subhierarchy that occur outside of an initial small group of erroneous concepts. We present examples of errors and examples of how corrections transform and simplify the partial-area taxonomy.

The notion of function is indispensable to our understanding of distinctions such as that between being broken and being in working order (for artifacts) and between being diseased and being healthy (for organisms). A clear account of the ontology of functions and functioning is thus an important d esideratum for any top-level ontology intended for application to domains such as engineering or medicine. The benefit of using top-level ontologies in applied ontology can only be realized when each of the categories identified and defined by a top-level ontology is integrated with the others in a coherent fashion. Basic Formal Ontology (BFO) has from the beginning included function as one of its categories, exploiting a version of the etiological account of function that is framed at a level of generality sufficient to accommodate both biological and artifactual functions. This account has been subjected to a series of criticisms and refinements. We here articulate BFO's account of function, provide some reasons for favoring it over competing views, and defend it against objections.

Business rules play a critical role in developing effective and flexible information systems. In light of this critical role, the publication of the Semantic Business Vocabulary and Business Rules standard (SBVR) is considered a highly significant advance. However, although SBVR is a major breakthr ough in defining business vocabulary and rules, its subsequent adoption in practical applications has proved to be much slower than originally anticipated. This paper proposes an approach that firstly reduces the SBVR complexity by identifying a subset that can be used to express structural design models unambiguously. Secondly, it formalizes a transformation that maps the identified SBVR subset to the UML class diagram, based on formally defined and validated meta-models that can guarantee the correctness of the generated UML class model. Finally, it incorporates the formal meta-models and the formal transformation algorithm into an automated transformation tool. The proposed approach is evaluated by taking into consideration the aspects of applicability, functionality, correctness, and completeness through a number of case studies and a comparison with an existing transformation approach.

One of the barrier of the Fashion, Textile and Clothing (FTC) domain is the multiplicity and the evolving vocabulary used by the industry players as well as stylists or common users. Some projects tried to tackle the challenge of the absence of common vocabulary and concepts shared at least by the professionals. However, the efforts to ensure a better interoperability and modelisation were limited. VetiVoc intends to overcome these limitations. This modular termino-ontological resource covering the different FTC fields, from the fiber to the fashion victim, from the shoes to the headwear, from the garment to the accessories offers a flowing consistency throughout the domain, a vocabulary that accommodates several natural languages (not only English) and an industrialized project (not a proof of concept). The useful semantic solutions offered include interoperability as well as indexing and information retrieval.

Over the last years, numerous ICT applications with mechanisms to detect situations have been developed to support disaster management (DM), which is a field of a great societal and economic importance. Those applications are termed situation-aware (SA) because they try, in near real-time, to perceive and comprehend a situation of some type (e.g. disease epidemics) and project a reaction to the detected situation (e.g. isolate diseased people). An obstacle to the modelling of SA applications is the lack of well-founded structural and temporal constructs, which is inherent to conventional design techniques. Ontology-driven conceptual modelling has been successfully applied to overcome this issue, where ontological analysis based on a foundational ontology supports the modelling of concepts within a specific field as a well-founded core ontology. In this paper we discuss the importance of a well-founded core ontology for DM to support the specification of SA applications. We give an overview of the comprehensive framework we are developing, in which the DM core ontology plays a prominent role in the development of SA applications. In particular, we discuss the challenge of harmonizing concepts related to the modelling of situations in a foundational ontology in the lights of the Barwisean situation theory, Situoid theory and Situation awareness theory. This challenge has to be addressed to properly support SA applications in DM.