One could argue that fictional characters in literary texts, films, television series, and other artistic works become part of our lives. Although they do not possess spatio-temporal existence like flesh-and-blood individuals, their presence can be traced in our cities through museums-such as the Sherlock Holmes Museum in London or Juliet's house in Verona-or through statues like the Rocky Balboa statue in Philadelphia. In this paper, we explore ontology-based approaches to representing and comparing interpretations of literary characters. In particular, we introduce and discuss several similarity measures between interpreted literary characters, and discuss conditions for the identification of characters across heterogeneous interpretations.
Processes are fundamental to enterprises, serving as significant engines of optimization and analysis. Understanding business processes is critical, yet integrating formal process ontologies with enterprise data and workflows remains difficult. We refer to this specific kind of ontology application, intended for practitioners working with enterprise data, as operational realization. The varied ontological commitments and highly expressive representation languages of process ontologies create barriers for operational realization, including issues of decidability, a lack of tooling, and operational constraints. This paper presents an architecture for the operational realization of process ontologies, driven by process mining needs. A key aspect of the architecture is the formalization of ontological commitments in tasks like data cleaning and analysis, which rely on implicit assumptions embedded in the interpretation of process data. Our approach builds on existing methodologies, notably ontology-based data access (OBDA), while going further by encoding domain knowledge required to interpret and reason with process data. This structure moves beyond an A-Box and T-Box distinction, explicitly capturing how the process ontology, domain data, and supporting data interpretation theories enable complex process reasoning. By structuring the dynamics of a process ontology, domain data, process data theories, and by characterizing reasoning scenarios, our approach provides a pragmatic foundation for integrating process ontologies into data-driven process workflows. We demonstrate this architecture with real enterprise data, challenge problems, and scenarios already widely used for benchmarking in process mining.
Are ontologies a tool to remove or to highlight the role of human judgement in data systems? This is the question posed in this lecture, which reviews efforts to build ontologies as tools to automate data analysis—or at least make it amenable to computational methods–and argues that ontologies can help to identify and articulate the role and significance of human intervention, values and goals in making sense of data. I exemplify this argument through a study of the Crop Ontology and its application to the study of plant traits. This ontology exemplifies what I call a process-sensitive approach to data semantics, in which the naming of the phenomena in question (plant traits) attempts to capture the environmental interactions (including those between plants, researchers and wider publics) instead of the biological products in and of themselves.
In recent developments of AI, we see an increasing augmentation of neural models with unstructured data (RAG), structured data (Graph RAG) and even agents, reasoning services and APIs (Agentic AI). In the evolving landscape of scientific knowledge management, the integration of neuro-symbolic and agentic AI approaches offers opportunities to enhance the organization, discovery, and synthesis of research contributions. We explore how knowledge graphs and large language models (LLMs) can be synergistically combined to advance the representation and accessibility of scholarly knowledge. At the heart of this approach is the Open Research Knowledge Graph (ORKG)—a platform that structures scientific knowledge into machine-readable representations, enabling comparative analyses, automated reasoning, and contextualized exploration of research findings. Extending this vision, ORKG ASK introduces a novel query and synthesis system, combining symbolic knowledge with neural AI capabilities to provide precise, explainable, and interactive responses to complex scientific inquiries. We will examine the foundations and practical applications of agentic and neuro-symbolic AI in scholarly knowledge organization. By bridging the gap between symbolic representations, agentic tools and neural models, this approach aims to make scientific knowledge more accessible, transparent, and actionable—paving the way for a new era of AI-driven research assistance.
Complex systems are full of unpredictable and uncertain behaviours that depend on many factors. Various fields of science have set themselves the task of studying these systems and predicting their behaviour under various premises with the help of computer models. Kwakkel et al. [1] published an uncertainty matrix to systematically record and communicate uncertainties about systems. This paper takes up the matrix, analyses the concepts of model and uncertainty in literature, and models them ontologically in the Modelling Uncertainties Ontology (MUNO), a BFO-based mid-level ontology for modelling uncertainties in models for many domains. MUNO is evaluated against requirements and competency questions. Based on the ontology, an RDF-shape is created and used as an example in a case study to annotate the uncertainties for an energy system model.
In this paper, I discuss the applicability of Quine's idea of ontological commitment to the realm of applied ontology. In particular, I put forward a certain extension of this notion, which, in my view, provides a more informative insight into how a given applied ontology represents the existential aspect of its intended domain. The theoretical considerations are illustrated with a case study, where I describe the ontological commitments of the DOLCE ontology.
This paper studies conceptual evolution and ways to capture concepts' continuity despite their change. The proposal laid down in this paper is to interpret concepts as variable embodiments as first introduced by Fine. The main question of the paper is how to understand the principle of variable embodiment in the case of concepts. The paper proposes to use a specific notion of conceptual coherence to constrain the principle of variable embodiment. In general, the idea is that concepts must retain some shared information at different points in time. Conceptual coherence is defined as an overlap in topic, specifically captured through the intersection of possible generalisations of a concept at different moments. To formalise this idea, a framework incorporating refinement operators is proposed and its applicability is illustrated through the analysis of various examples from the literature.
Since the emergence of the field of eXplainable Artificial Intelligence (XAI), a growing number of researchers have argued that XAI should consider insights from the social sciences in order to adapt explanations to the expectations and needs of human users. This has led to the emergence of a field called Social XAI, which is concerned with understanding how explanations are actively shaped in the interaction between a human user and an AI system. Recognizing this turn in XAI toward making XAI systems more "social" by providing explanations that focus on human information needs and incorporating insights from human-human explanatory interactions, in this paper we provide a formal foundation for Social XAI. We do so by proposing novel ontological accounts of the key terms used in Social XAI based on Basic Formal Ontology (BFO). Specifically, we provide novel ontological accounts for explanandum, explanans, understanding, explanation, explainer, explainee, and context. In doing so, we discuss multifaceted entities in Social XAI (having both continuant and occurrent facets; e.g., explanation) and the relationship between understanding and explanation. Additionally, we propose solutions to seemingly paradoxical views on some terms (e.g., social constructivist vs. individual constructivist perspective on explanandum).
Contamination by heavy metals, per- and polyfluoroalkyl substances (PFAS), and other emerging pollutants poses serious risks to environmental and human health. Effective monitoring and tracing require integrating data from diverse sources. A knowledge graph approach enables semantic integration, but relies on an ontology that supports intuitive and robust querying and reasoning. To address this, we present the Contaminant Observations and Samples Ontology (ContaminOSO), a framework for semantically enriching environmental contaminant data. Built on SOSA and QUDT ontologies, ContaminOSO introduces key extensions to meet contamination-specific needs and real-world data challenges. This paper highlights four of its core design solutions: (1) extending SOSA to model multiple features of interest; (2) using QUDT to standardize the representation of contaminants and observed properties; (3) developing a detailed and nuanced pattern for measurement result representation using QUDT and STAD; and (4) adopting a pragmatic approach for connecting to existing taxonomies from the OBO Foundry, such as the NCBI organismal classification and relevant subsets of the Food Ontology (FoodOn), for classifying samples.
Davidsonian event semantics has become immensely influential in contemporary natural language semantics. On that view, all events (including acts and states) are considered the same ontologically, i.e., Davidsonian particulars, and they all serve as arguments of verbs and corresponding nominalizations. We argue that that view has to be rejected both ontologically and on the level of compositional semantics. We argue that natural language reflects two related ontological distinctions: first a distinction between base events (BE) and plan events (PE) (which are based on plans or procedures); second a distinction between acts and intentional acts. We propose an ontological account of PEs and intentional acts based on Fine's notion of a qua object: a PE is a BE qua realizing a plan and an intentional act is an act qua realizing an intention. The BE-PE distinction manifests itself in existence predicates for events and predicates of participation (take part in, participate, be part of). Unlike Fine, we allow properties of qua events to be inherited not only from their base (temporal part structure), but also from their gloss, namely as a 'slot mereology' consisting of plan-specific roles, which can be specified for particular individuals or be open. Whereas the ordinary noun part (as in the parts of) picks out parts of the former part structure, the light noun part in the predicates take part in and be part of picks out roles inherited from the plan. Whereas take part in can relate to both specified and open roles, be part of can only target specified roles. The distinction between BEs and PEs is encoded in lexical semantics only; the distinction between acts and intentional acts is also encoded in syntax, in the presence of a silent operator. This requires a more complex form of event semantics than standard Davidsonian semantics.
gist is an open-source, business-focused ontology actively developed by Semantic Arts. Its lightweight design and use of everyday terminology has made it a useful tool for kickstarting domain ontology development in a range of areas including finance, government, and pharmaceuticals. The Basic Formal Ontology (BFO) is an ISO/IEC standard upper ontology that has similarly found practical application across a variety of domains, especially biomedicine and defense. Given its demonstrated utility, BFO was recently adopted as a baseline standard in the U.S. Department of Defense and Intelligence Community. Because BFO sits at a higher level of abstraction than gist, we see an opportunity to align gist with BFO and get the benefits of both: one can kickstart domain ontology development with gist, all the while maintaining an alignment with the BFO standard. This paper presents such an alignment, which consists primarily of subclass relations from gist classes to BFO classes and includes some subproperty axioms. The union of gist, BFO, and this alignment is what we call "gistBFO." The upshot is that one can model instance data using gist and then instances of gist classes can be mapped to BFO. This not only achieves compliance with the BFO standard; it also enables interoperability with other domains already modeled using BFO. We describe a methodology for aligning gist and BFO, provide rationale for decisions we made about mappings, and detail a vision for future development.
In a keynote at FOIS 2024, Oystein Linnebo proposed and motivated the reassessment and reconstruction of foundational formal ontologies via the constructive approaches exploited in philosophical logic. The proposal has attracted attention due to a series of potentially positive consequences: conceptual clarity on the adopted entities and constructors, structural clarity regarding the ontology organisation, completeness of the ontology relative to the combinatorics of elements and operators, and conceptual and logical consistency of the whole system. It remains unclear how the constructive approach would work on today's foundational ontologies, and how it can cope with the concerns that led to building such systems. To start addressing this issue, in this paper we consider three approaches on foundational ontologies like BFO, DOLCE and UFO, to investigate how these systems might be seen as contructive-like structures. This analysis is a first step towards Linnebo's proposal, it serves to highlight aspects that require more investigation to implement the proposal, and suggests what kind of work and considerations are needed to turn today's foundational formal ontologies into constructive-based ontological systems.
Data, specially those related to confidential information, plays a crucial role in contemporary organizational operations. The need to exchange information among modern entities in a multi-national context has led to the emergence of the International Data Spaces (IDS) as an option that promotes security, sovereignty, and reliability in data exchange. Enabling organizations to understand the value generated by the data and the relationships between the parties involved is a major challenge, which can be addressed through explicit business models. The objective of this research is to develop a solution to derive and formalize business models for data-sharing agreements in IDS. The Design Science Research Methodology was adopted. During the problem investigation phase, we identified the lack of explicit business models for IDS and its associated requirements. In the treatment design phase, we applied the Systematic Approach for Building Ontologies to develop the Soberana ontology that serves as a model factory for business models in IDS. Finally, in the treatment validation phase, we used SPARQL to demonstrate how the ontology addresses the competency questions covered by the Ontology Requirements Specification Document. A practical demonstration of the use of the ontology with synthetic data resulted in the derivation of data-sharing agreement models in different ecosystem organizations present in the literature. The Soberana ontology provides a contribution to enable organizations to formulate business strategies, understand the functioning of IDS, and analyze data-sharing agreements.
Ensuring the logical coherence of subsumption hierarchies is essential for high-quality ontology engineering. While validation frameworks such as OntoClean provide formal mechanisms for checking taxonomic consistency in endurant entities, there remains a critical gap in validation tools for perduring entities such as processes, events, and states. This paper introduces a principled framework for validating perduring entity subsumption hierarchies by leveraging meta-properties, including cumulativity, homeomericity, atomicity, and agentivity. The framework defines a set of meta-property constraints that enforce consistency in the subsumption hierarchy of perdurants. Case studies demonstrate its utility across both foundational ontologies and domain-specific levels. Notably, when applied to the uppertier structure of DOLCE, the framework preserves its internal stratification while exposing classification gaps. Furthermore, applying the framework to the MAVEN event-type hierarchy reveals ontological inconsistencies introduced by human developers, demonstrating the framework's effectiveness as a verification tool. This work establishes a foundation for the meta-property-driven construction and validation of perdurant taxonomies, addressing a long-standing gap in formal ontology validation methodologies.
Despite its central importance for an ontology of food and food production, the notion of food product remains ambiguous. In this work, we develop two ontological approaches to food products within the framework of the upper ontology Basic Formal Ontology (BFO), aiming to enrich the axiomatization of the class Food product in the existing BFO-compliant ontology FoodOn. The first, a derivation-based approach, characterizes food products as being intentionally "derived from" food materials. The second, a role-based approach, analyzes them as food materials that bear a "product role". We propose to elaborate these approaches to food products by introducing the relation "derives from part of" for the derivation-based approach and the entity "consumption product role" for the role-based approach. We evaluate the strengths and weaknesses of each approach and propose possible strategies for addressing their respective weaknesses.
This position paper contends that as artificial intelligence (AI) and robotics advance at a rapid pace-transforming industries, healthcare, transportation, and more-the development of structured knowledge and formal ontologies struggles to keep up. This imbalance poses substantial risks, as increasingly powerful and autonomous systems can make decisions that are both opaque and difficult to verify. While AI and robotics research benefit from significant funding and widespread attention, ontology and structured knowledge efforts often remain under-resourced, under-integrated, and often sidelined-leaving a growing semantic gap in system design and governance. To address this gap, we argue that formal ontology research must be reinvigorated to keep pace with the accelerating demands of AI and robotics-not merely as a support function but as a core contributor to trustworthy and beneficial AI/robotics system design. This requires renewed investment in the academic foundations of ontology engineering, reintegration of semantic modeling into AI/robotics development workflows, and the lowering of practical barriers that have hindered broader adoption. Specifically, we advocate for ontology-augmented verification methods that incorporate semantic constraints into behavioral validation; for the institutionalization of semantic auditing practices that allow ontologies to serve as transparent, inspectable system referents; and for the adoption of FAIR publishing standards that treat ontologies as first-class research outputs. These directions, we argue, are essential to bridge the gap between AI/robotics and ontology research and to ensure that autonomous AI/robotics systems remain not only performant but also transparent, accountable, and ultimately beneficial to humanity.
OntoUML is an ontology specification language for structural conceptual modelling based on the Unified Foundational Ontology (UFO). It extends UML to capture precise semantics about a domain. For OntoUML to add value in software development, its semantics should align with the actual code. To achieve this, in this research we developed an automated, semantics-preserving transformation from OntoUML to Java code that can be used in conjunction with existing OntoUML tools. The transformation is based on the Eclipse Modelling Framework (EMF) and includes parsing of the OntoUML JSON schema, an UML-based object-oriented implementation metamodel, and Java code generation. It has been validated by executing it on publicly available models from the OntoUML model catalogue, of which 82 models were transformed and checked for superficial errors and compatibility of the generated code. For five of these models, the code was manually inspected in more detail. The main contributions of this research are: transformation rules for 12 OntoUML stereotypes; an EMF Ecore metamodel for OntoUML that can be reused in other model transformation projects; and a complete implementation of the OntoUML-to-Java transformation.
We explore the concept of meaning in applied ontologies using possible world semantics. We begin by analyzing the nature of possible worlds in the influential framework proposed by Guarino, Oberle and Staab: interpreting an ontology involves selecting, from the set of logically possible worlds, a subset of worlds that are metaphysically possible according to the ontology - ideally corresponding to the set of worlds that are metaphysically possible according to the ontology's creator. We argue that this framework is limited to analytic statements and should be extended to encompass synthetic statements. The analytic/synthetic distinction, we suggest, can itself be understood in terms of the necessary/contingent distinction using metaphysically possible worlds. We propose a dual framework for introducing terms in ontology development, integrating both descriptivism and Kripke's theory of rigid designators. This framework accommodates a posteriori analytic statements, implying that the meaning of a term may be unknown even to its creator. Finally, we distinguish two distinct roles that labels can play in ontology development: either as rigid designators that carry semantic weight, or as mere human-readable tags serving as proxies for underlying descriptions.
In this paper, we present a preliminary ontology of bias based on the DOLCE foundational ontology. The main reason for devising such an endeavour is to make explicit the ontological assumptions behind the use of terms indicating the elements composing a biased outcome. Firstly, we discuss what the object of a bias is -namely, the entity that might be deemed biased, which we identify with situated inferences, i.e. propositional contents that can be asserted by some (human or artificial) agent from other propositional contents. We will thus categorise in DOLCE various types of biases as concepts that classify situated inferences. The content of such inferences is then associated with the following elements: i) the agent responsible for drawing the conclusion, ii) the objects and iii) the concepts used in the premises and in the conclusion of the inference, iv) the time when the inference takes place. These ingredients will serve to trace the origin of what we shall call a biased inference back to any of the above elements, relating some of the biases present in the literature to these ontologically founded elements.
Ontologies often involve complex logical structures, so changes to individual classes or the addition of new axioms can have significant implications for other parts of the ontology. Due to this complexity, dependencies between symbols in the vocabulary of the ontology are not always immediately apparent. In this paper, we define three semantics-based approaches for establishing dependency relationships between these symbols and explore their specific properties. Additionally, we apply these dependency relations in a case study.
