Functional locations are persistent positions in industrial facilities where maintainable items are installed. Industrial data models consistently separate the position from the equipment installed at it, yet implementations often collapse the two, breaking historical continuity when equipment is replaced. This paper presents an ontology design pattern that separates five concerns: the persistent functional location itself, the functional location code placing it in the facility hierarchy, the equipment tag encoding its expected equipment type, the functional location specification prescribing its requirements, and the installed equipment that transiently occupies it. We first characterize the pattern independently of any foundational ontology, showing that the same separation recurs across industrial data models and prior conceptual analyses. We then provide two groundings. In BFO/IOF, an ontological elimination argument shows that bfo:Site is the only category adequate for the position; the code and the tag are two kinds of registered identifiers that both denote that same site, and the specification is a directive information entity. In the Industrial Data Ontology (ISO/FDIS 23726-3), we map the pattern onto lis:Site and the Specified/Actual machinery, discussing the successes and limitations of each grounding. We validate the pattern with SPARQL queries over operational data from an offshore petroleum platform, demonstrating maintenance-history continuity across documented equipment replacements.
Industrial reference data libraries (RDLs) such as ISO 15926-4, CFIHOS, and DEXPI provide thousands of equipment classes for the process industries, yet these classes lack formal ontological axioms: their definitions are natural-language tautologies that support neither automated reasoning nor cross-standard interoperability. We present a four-step method for enriching flat RDL classes with BFO-grounded axioms using IOF-Core as the mid-level vocabulary. The method rests on two constructs: the identity-giving function, which grounds the identity of each equipment universal in its designed purpose, and the process participation signature, which formalizes the characteristic pattern of inputs, outputs, and transformations of the process that the function realizes. Together they yield a principled, physics-grounded criterion for class disjointness that goes beyond stipulation. We apply the method to ten equipment types central to petroleum production, producing an OWL 2 DL ontology with 99 named classes, 16 disjointness axioms, and SKOS links to ISO 15926-4, DEXPI, and CFIHOS, evaluated through consistency checking, instance-level (ABox) classification tests, and six competency questions covering reasoning tasks that flat RDLs cannot support. The axiomatization also yields reusable ontological patterns, including structural parallels differentiated solely by function and a principled genus-level split between assemblies and monolithic artifacts. We argue that enrichment—adding formal semantics to existing RDL classes rather than building competing taxonomies—is the most productive path for bringing ontological rigor to industrial data integration. The ontology is publicly available, bilingual (English and Brazilian Portuguese), and annotated following IOF conventions.
The recent surge of interest in eXplainable Artificial Intelligence (XAI) has led to a growing focus on incorporating social insights into XAI and grounding explainability in social interaction. This has given rise to the concept of Social XAI. However, ontological modelling of the Social XAI framework remains limited. In particular, there is a lack of models that capture the progressivity of explanation processes in terms of the changes that occur. This paper introduces the Social XAI Ontology, a BFO-based reference ontology. The ontology models a variety of entities involved in the process of explanation. First, it provides a model of the participants in explanatory dialogues, such as artificial explainers and human explainees, and the roles they may take. Then, it introduces a rich set of explanatory moves that explainers and explainees perform. The paper further presents ontological accounts of four types of context that are central to real-world Social XAI projects. Furthermore, it provides an ontological account of different dimensions of adaptivity, which is considered to be an essential prerequisite for realizing Social XAI systems. Finally, the paper revisits two existing models of understanding in Social XAI by applying a recent theory of change. The paper concludes by discussing use cases for the Social XAI Ontology.
Lately, people have come to routinely interact and rely on artificial intelligence tools. Hence, precise accounts of intuitive social and epistemological phenomena are needed. In this paper, a philosophically robust logic of rumors is presented as a contribution to formal ontologies of social entities. Rumors are considered to be three-way relations that hold between a group of rumorers, a proposition being spread, and a time period. A general bare-bones notion of rumors is defined via necessary and sufficient conditions. Thence, two logics of rumors are formalized: a first-order logic to cater to practicality issues and a second-order logic, with generalized quantifier “most”, to reason about a more natural notion of rumors. Several modalities are employed such as information flow, verification, plausibility, and a non-normal notion of goodness. Finally, interesting properties about rumors are presented that deepen our understanding of these phenomena.
Industrial CAD/STEP models provide high-fidelity geometry and product structure, yet automated assembly/disassembly planning also requires machine-interpretable semantics about how parts are connected (e.g., fastening, retention, kinematic guidance) and which operations must precede others. We present an ontology design pattern grounded in DOLCE+DnS Ultralite (DUL) that represents each assembly connection as a reified joint situation, annotated with STEP-derived interface geometry, and optional descriptors such as retention method and degrees of freedom. From these structured connection descriptions, we derive a connection-aware precedence matrix. Hard (MUST) constraints are validated by geometric feasibility checks, while soft (DEFAULT) precedence candidates are generated by reusable templates over joint descriptors and can be overridden when evidence conflicts. A gearbox case study illustrates how coupling-mediated, access-closure, and shaft-mount patterns yield an explainable partial ordering suitable for precedence compilation. The resulting framework complements CAD/STEP with explicit, auditable connection semantics for precedence generation.
