Applied Ontology

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.

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.

Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors, where legal thresholds determine access to protection, healthcare, and judicial procedures. Dental age assessment is widely recognized as one of the most reliable biological approaches for adolescents and young adults, but current practices are challenged by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations hinder transparency and reproducibility, amplified by the increasing adoption of AI-based methods. The AIdentifyAGE ontology is domain-specific and provides a standardized, semantically coherent framework, encompassing both manual and AI-assisted forensic dental age assessment workflows, and enabling traceable linkage between observations, methods, reference data, and reported outcomes. It models the complete medico-legal workflow, integrating judicial context, individual-level information, forensic examination data, dental developmental assessment methods, radiographic imaging, statistical reference studies, and AI-based estimation methods. It is being developed together with domain experts, and it builds on upper and established biomedical, dental, and machine learning ontologies, ensuring interoperability, extensibility, and compliance with FAIR principles. The AIdentifyAGE ontology is a fundamental step to enhance consistency, transparency, and explainability, establishing a robust foundation for ontology-driven decision support systems in medico-legal and judicial contexts.

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.

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.

Major depressive disorder is often treated with medication. These antidepressants can cause side effects affecting health and lifestyle. Dietary interventions can support traditional treatments, enhancing overall health. However, current food recommendation systems lack personalized menu options for those with mental health issues. This research focuses on designing a dietary recommendation model for depression. The main theoretical implication is a novel food menu inferring process using a food knowledge graph with semantic rules called FONDUE (FOod kNwleDge graphs with semantics rUlEs). The design of the food knowledge graph focuses on dietary restrictions in both mental and physical conditions. The semantic rules are created using descriptive logic and the SPARQL query language to infer diets appropriate for patients with depression. The FONDUE was evaluated by analyzing thirty common patient case studies, each representing different physical conditions in patients with depression. The evaluation results revealed that the accuracy of the retrieved relevant menus was high, with an average precision of 0.86. However, the completeness of the retrieved relevant menus was moderate, with an average recall of 0.67. The lower recall observed can be attributed to the numerous menus within the model, which results in many relevant options still needing to be extracted. The average F-measure, which reflects a balance between precision and recall, scored 0.74. Compared with ChatGPT and Google Gemini, FONDUE demonstrated superior performance. Consequently, the personalized dietary recommendation model effectively suggests food menus tailored to address patients’ physical and mental health needs, instilling confidence in its efficacy.

This is the second volume of the Special Issue celebrating 10 years of the Joint Ontology Workshops (JOWO). As in the first volume, the second volume includes extended versions of selected papers that were originally presented at JOWO workshops. In this editorial, we moreover offer a retrospective view of the history of the venue, an overview of the variety of topics it has hosted, and an analysis of their evolution over time, reflecting on the development of the community and the diversity of research it has fostered. Together, these elements provide a picture of JOWO as a space where foundational concerns coexist with a broad and expanding range of more applied ontology-related research themes. The papers collected in the two volumes of the Special Issue illustrate both the continuity of core topics and a growing openness towards application-oriented work, as well as the exploratory role of JOWO as a venue for emerging ideas. This editorial thus introduces and situates the contributions in this volume within the broader trajectory of the JOWO community.

The concept of situation is crucial for many domain ontologies, particularly in complex fields such as biomedicine. For instance, work on the Cell Tracking Ontology (CTO) revealed that the notion of situation is fundamental for the adequate representation of cellular genealogies, which provide a detailed record of the lineage and development of cells over time. Related findings were observed in other biomedical use cases, such as surgical navigation, medical risk identification, and pregnancy care and complication prevention. The development of these applications heavily rests on the General Formal Ontology (GFO, version 1.0), which effectively represents situations. However, different terminologies and modeling variations have been used across these applications, leading to modifications and extensions of GFO. We aim to generalize the results from CTO and other GFO applications towards a systematic basis for situation modeling. The paper reviews four biomedical projects using GFO for situation modeling and discusses the GFO theory of situations, along with its underlying intuitions. Based on this, we introduce a family of ontology design patterns tailored to modeling situations, which can be challenging, as an optimal approach often depends on the specific use case and requirements. However, the range of approaches observed in real-world scenarios is sufficiently limited to allow for a systematic classification of the modeling methods, addressing various levels of detail. Therefore, the family of patterns aims at providing a toolbox for depicting situations at different granularities, including their participants, qualities, dynamics, and changes over time, rather than forcing a one-size-fits-all approach.