We provide a theoretical foundation for a formal ontology of artifacts based on the notion of a realizable entity: a property that can be realized in associated processes of a specific correlated type in which the bearer participates. This realizable-centered approach to artifacts aims to accommodate a wide range of artifacts, from technical artifacts (e.g., screwdrivers) to artworks (including ready-made artworks) and spiritual artifacts (e.g., amulets), making it applicable across diverse fields such as engineering, art, anthropology and archeology. It is motivated by limitations of function-based accounts of artifacts and the needs for a meticulous analysis of their intentional dimension. To articulate various usages of the term “artifact,” we introduce two key notions: canonical artifact (an entity intentionally produced for a specific purpose) and usefact (an entity intended to be used for some accidental purpose). We provide a realizable-based characterization of both, classifying intentions as a subtype of realizable entities. We argue that our realizable-centered approach is more general than three prominent function-based theories of artifacts: designer-based, etiological and systemic. We further demonstrate that our framework can be adapted to Basic Formal Ontology (BFO), highlighting its flexibility and broad applicability.

Burden is a key concept in healthcare research, reflecting the challenges that illness and its management impose on patients, caregivers, and healthcare systems. While burden has been the focus of considerable scientific and clinical attention, the burden concept has attracted little in the way of theoretical attention. This has led to the absence of definitional consensus, which has, in turn, complicated the effort to provide ontological support for burden-related research. The present paper seeks to address these gaps by introducing the Biomedical Burden Ontology (BBO), a formal framework designed to represent and integrate burden-related data within biomedical informatics. The BBO is grounded in the Atlassian view of burden, which conceptualises burden as an individual’s obligatory participation in non-preferred processes. The ontology is implemented in the Web Ontology Language (OWL) and leverages Basic Formal Ontology (BFO), alongside existing ontologies such as the Mental Functioning Ontology (MFO) and the Emotion Ontology (MFOEM). Additionally, the BBO incorporates insights from predictive processing theories of brain function, framing burden as a disruption of an individual’s capacity to fulfil ‘optimistic’ predictions. By providing a structured approach to representing burden, the BBO facilitates research into patient experience, supports the development of minimally disruptive medicine, and enables more effective measurement of burden in clinical and policy contexts.

The hard problems of robotics occur at the interface of an artificial agent and its surrounding physical world: perception and action. These are often regarded as tasks requiring tacit, unformalized and possibly unformalizable knowledge, making ontology engineering of apparently little value for robotics. We argue instead that an important role has appeared for ontology in current developments in robotics. Recent AI techniques such as foundation models have revealed the potential of explicit knowledge to be useful at all parts of a robotic system, but these techniques will require assistance from formal, verifiable methods to produce trustworthy systems. Further, robotics can contribute to the development of ontology engineering. As robotic agents become more autonomously capable, questions about the meaning of agency and responsibility become increasingly pressing, and will require careful consensus building across a variety of stakeholder groups. Finally, robotics may be a challenging test ground for the philosophical assumptions underlying foundational ontologies, and can thus assist the ontology engineering community in better understanding the consequences of ontological modeling decisions more generally.

Recognizing a lack of formal knowledge organization systems for digital forensic artifacts, this paper proposes the Autopsy Ontology , which, for the first time, defines the de facto standard Autopsy tool’s terminology in OWL. This ontology was designed to be used for automated reasoning over, and advanced querying of, digital forensic artifacts analyzed in Autopsy, and generating semantic knowledge graphs of digital forensic datasets in resource description framework.

Electronic commerce (e-commerce) has grown significantly since the first online shops appeared. Such a growth is quantifiable not only in terms of users and sales but also in complexity: assets, supply chains, shipment modalities and payment methods, auctions, digital negotiations and blockchains are examples of how e-commerce is evolving in the WEB 3.0 era. As consequence of the growth and spread, the need of realizing trustworthy marketplaces, especially when decentralized technologies are involved, came forward. Semantic Web technologies may play a crucial role in this mission as their adoption by the major online marketplaces evidenced. The GoodRelations ontology is a milestone in this context. However, many limitations prevent GoodRelations from addressing the challenges of the incoming releases of the WEB: indeed, intricate and intertwined relationships among the digital commerce stakeholders are outside the expressive power of GoodRelations. Improvements to ontological representation in the e-commerce realm derive from the Theory of Agents. Among the available models, the behavioristic approach pursued by the Ontology for Agents, Systems, and Integration of Services ( OASIS ) is well suited to addressing emerging challenges, including those posed by Web 3.0. In this contribution, we present an extension of OASIS for the e-commerce domain, aimed at supporting next-generation digital commerce by incorporating features such as supply chain modeling, distributed ledgers, negotiation mechanisms, and auction processes.

Activity Streams is a data format designed to describe activities. Although its specification is written in natural language, its core vocabulary is formally defined in an OWL (Web Ontology Language) ontology. In this work, we propose a set of additional OWL axioms to extend the existing ontology, with the goal of enabling more detailed, precise, and machine-interpretable descriptions of Activity Streams. This enhancement aims to support real-world applications, such as those related to automatic test generation from OWL specifications.

Intellectual property (IP) plays a crucial role in fostering innovation and economic growth. However, the complexity of IP rights and their legal frameworks across different jurisdictions poses challenges for standardization and interoperability. Ontologies have emerged as a powerful tool to address these challenges. This study, through a literature review, aims to identify and analyze existing ontologies in the IP domain, evaluating the predominance of researchers/groups, application contexts, legal frameworks, upper-level ontologies, conceptual coverage, representation languages, and adopted formalisms. The analysis reveals core conceptual elements, including IP rights, agents, events, conditions, agreements, and works of mind, which define the dynamics of IP life cycles. We identified a greater focus of the existing ontologies on Copyright when contrasted with aspects of Industrial Property. More ontologies addressed general legal frameworks rather than country-specific regulations. Additionally, the development of IP ontologies remains concentrated in a limited number of countries and research groups, indicating the need for broader collaboration. Many ontologies prioritize implementation aspects over conceptual clarity, potentially affecting interoperability. Despite these challenges, IP ontologies play a crucial role in supporting standardization, legal compliance, and data integration. The findings emphasize, among others, the importance of refining conceptual models, promoting harmonization to enhance semantic interoperability.

Station–city integration cyberspace behaves as an interdisciplinary field of intelligent transportation and smart city, also a representative scenario in the Architecture, Engineering and Construction (AEC) sector. Due to the growing demands in data integration research, the intelligent operation and maintenance (O&M) of the station–city integration cyberspace needs to implement semantic ontology, which is suitable for semantic web construction. To achieve semantic information fusion of multi-source heterogeneous data, and clarify the decision-making role of various types of data on specific operational goals, this article proposed a framework for semantic ontology model construction, based on the deployed sensor network. Specifically, an ontology model for station–city integration cyberspace O&M was constructed, incorporating sensor data mainly from five categories, named structure, environment, crowd flow, emergency events, and energy consumption, respectively. Subsequently, the r d f l i b library in Python was utilized to assign data flow to static semantic models. Furthermore, a semantic web inference engine was generated using decision rules, ultimately completing risk early warning and equipment maintenance for the sensor network. Finally, a case study was conducted for the station–city integration O&M scenario in Shenzhen North Station, and the experimental results demonstrated the applicability and effectiveness, providing robust, intelligent data support for intelligent O&M.

Ontologies enable knowledge sharing and interdisciplinary collaboration by providing standardized, structured vocabularies for diverse communities. While logical axioms are a cornerstone of ontology design, natural language elements such as annotations are equally critical for conveying intended meaning and ensuring consistent term usage. This paper explores how meaning is represented in ontologies and how it can be effectively represented and communicated, addressing challenges such as indeterminacy of reference and meaning holism. To this end, instead of following the conventional approach of beginning with existing ontologies and working toward alignment or modularization, this article proposes a reversal of perspective: Taking the ontological term as the starting point and introducing a new structure, named “ontological unit,” characterized by: A term-centered design; enhanced characterization of both formal and natural language statements; and an operationalizable definition of communicated meaning based on general assertions. By formalizing the meaning of ontological units, this work seeks to enhance the semantic robustness of terms, improving their clarity and accessibility across domains. Furthermore, it may offer a more effective foundation for ontology generation and significantly improve support for key maintenance tasks such as reuse and versioning. This article aims to establish the theoretical groundwork for the proposed approach and to lay the foundations for future applications in applied ontologies.

We propose an axiomatic ontological framework for both informational templates and the substantial informational entities (named “fillers”) that can comply with such templates. The mereological structure of a filler is provided by its slots, following seminal work by Bennett and the mereology of slots approach by Tarbouriech et al. Templates are composed of placeholders satisfying an extensional mereology. The parthood relation between placeholders is mirrored into the parthood between slots of the fillers compliant with those placeholders, where if a filler x complies with a placeholder a , the relation of mirroring is an isomorphism between a subset of slots of x and the template-parts of a . Some placeholders are mandatory and are mirrored into slots that need to be filled by a non-empty filler, whereas others are optional and are mirrored into slots that can be filled by an empty filler. We discuss mereological sum among placeholders and slots, order considerations, the distinction between empty fillers and empty concretizations (such as spaces or silences), the notion of semantic compliance and applications to clinical documents, relational databases and linguistics.