Towards Formalisation of Concept Descriptions and Constraints

The integrated management of industrial systems in future environments like Industry 4.0 requires the effective management of information throughout the engineering life cycle. As systems pass through phases of design, construction, operation, maintenance, renewal or replacement, they will be administered via different information ecosystems, requiring changing perspectives on their descriptive information. A central role in the interplay of software and hardware artefacts, functions, documentation and managing software is played by the descriptions of concepts (i.e. formalised definitions of concepts within the domain of quantification). In this paper we propose a unified formalisation of descriptions that permits consistent analysis of the relationships between the designs, types, products, and concrete artefacts that can be found in the industrial engineering life-cycle. The approach is consistent with our earlier framework that describes artefacts, requirements and functional roles in the context of the DOLCE foundational ontology.

UFO: Unified Foundational Ontology

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.)

DOLCE: A descriptive ontology for linguistic and cognitive engineering1

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.

Semantic Description of Explainable Machine Learning Workflows for Improving Trust

Explainable Machine Learning comprises methods and techniques that enable users to better understand the machine learning functioning and results. This work proposes an ontology that represents explainable machine learning experiments, allowing data scientists and developers to have a holistic view, a better understanding of the explainable machine learning process, and to build trust. We developed the ontology by reusing an existing domain-specific ontology (ML-SCHEMA) and grounding it in the Unified Foundational Ontology (UFO), aiming at achieving interoperability. The proposed ontology is structured in three modules: (1) the general module, (2) the specific module, and (3) the explanation module. The ontology was evaluated using a case study in the scenario of the COVID-19 pandemic using healthcare data from patients, which are sensitive data. In the case study, we trained a Support Vector Machine to predict mortality of patients infected with COVID-19 and applied existing explanation methods to generate explanations from the trained model. Based on the case study, we populated the ontology and queried it to ensure that it fulfills its intended purpose and to demonstrate its suitability.

CargO-S: A pattern-based well-founded legal domain ontology for the traceability of goods in logistic sea corridors

Building legal domain ontologies is a prominent challenge in the ontology engineering community. The ontology builders confront issues such as the complexity of the legal domain, the difficulty of applying existing ontology engineering approaches, and the intention of developing legal models faithful to realities. In this paper, we discuss constructing a well-founded legal domain ontology, named CargO-S, for the traceability of goods in logistic sea corridors. For building CargO-S, a pattern-oriented approach is applied, supported by ontology-driven conceptual modeling, ontology layering, and ontology reuse processes. CargO-S is grounded in the unified foundational ontology UFO by using the ontology-driven conceptual modeling language OntoUML. Besides, ontology layering is proposed to simplify the development process by dividing CargO-S into three layers located at different granularity levels: upper, core, and domain. For building the upper and core layers, conceptual ontology patterns are reused from the foundational ontology UFO and the legal core ontology UFO-L. These patterns are applied, either by extension or analogy with legal rules, for building the domain layer. CargO-S is then validated by implementing the ontology as OWL and SWRL rules. Finally, the performance and the semantic accuracy of CargO-S are evaluated using a dual evaluation approach.

Remembering Simone de Beauvoir’s ‘ethics of ambiguity’ to challenge contemporary divides: feminism beyond both sex and gender

This article returns to Simone de Beauvoir’s philosophical oeuvre in order to offer a way of thinking beyond contemporary feminist divisions created by ‘gender critical’ or trans-exclusionary feminists. The ‘gender critical’ feminist position returns to sex essentialism to argue for ‘abolishing’ gender, while opponents often appeal to proliferated gender self-identities. I argue that neither goes far enough and that they both circumscribe utopian visions for a world beyond both sex and gender. I chart how Beauvoir’s ontological, ethical and political positions can be used to overcome the material/cultural, sex/gender bind that the contemporary divide perpetuates. I outline Beauvoir’s ‘ambiguous’ non-foundational ontology that attends to both the cultural origins, and material effects, of both sex and gender, and to the extent that humyns are fundamentally social. After outlining Beauvoir’s definition of freedom as purposive action, I then outline how the existence of the humyn-made and intersubjectively-upheld ‘situations’ of both sex and gender delimit this, urging feminists to return to the lost question of eradicating both. I use the utopian impulse in Beauvoir to argue that an ethics of reciprocity is an alternative mode of understanding the self and others. Beauvoir also calls for a political strategy that I call a ‘utopian realism’ that I apply to the contemporary divide. A way forward that is attentive to the concerns of both positions is the pragmatic use of identity politics that is nonetheless mindful of identity’s limits, alongside Beauvoir’s proto-intersectional vision of solidarity politics based not on identity but on a position of alterity and shared political strategy. Ultimately, I use this to argue that feminism would do better to unite around a shared commitment to challenging alterity, rather than further contributing to it.

Needs and Intentionality

A thorough understanding of what needs are is fundamental for designing well-behaved information systems for many social applications and in particular for public services. Talking about needs pervades indeed the jargon of Public Administrations when motivating their service offering. In this paper, we propose an ontological analysis of needs, aiming at a principled disentangling of the different uses of the term. We leverage philosophical tradition on intentionality, for its rich understanding of mental entities, we compare it with the well-established BDI (Belief-Desire-Intention) tradition in knowledge representation, and we propose a formalisation of needs within the foundational ontology DOLCE. Throughout the paper, we motivate our analysis focusing on needs in public services.

WESgraph: a graph database for the wind farm domain

The construction and management of a wind farm involve many disciplines. It is hard for a single designer or developer to keep an overview of all the relevant concepts, models, and tools. Nevertheless, this is needed when performing integrated modeling or analysis. To help researchers keep this overview, we have created WESgraph (the Wind Energy System graph), a knowledge base for the wind farm domain, implemented as a graph database. It currently contains 1222 concepts and 1725 relations between them. This paper presents the structure of this graph database – content stored in nodes and the relationships between them – as a foundational ontology, which classifies the domain's concepts. This foundational ontology partitions the domain in two: a part describing physical aspects and a part describing mathematical and computational aspects. This paper also discusses a number of generally difficult cases that exist when adding content to such a knowledge base. This paper furthermore discusses the potential applications of WESgraph and illustrates its use for computation pathway discovery – the application that triggered its creation. It also contains a description of our practical experience with its design and use as well as our thoughts about the community use and management of this tool.

An Ontology-Based Approach for Production Planning and Control of Cyber-Physical Production Systems

The objective of this research is to develop a new ontology-based approach for the management and control of cyber-physical production systems (CPPSs). In the CPPSs, the management and control functions are integrated with a physical part of manufacturing system. The function of production planning and control of manufacturing systems (PPC) is an important part of the management and control of the CPPSs. The elements of the cyber system structure enable the dynamic management and control of manufacturing systems in real time, through the realization of the digitalized and cybernated functions of PPC. The proposed approach to management and control of the CPPSs is based on the foundational ontology of manufacturing systems. The digitalized production planning, scheduling, and control functions are implemented as a multi-agent system (MAS). The communication between agents was addressed to support the autonomic decision for each individual agent. A case study demonstrates feasibility of the approach through the use of simulation experiments.