The field of computational complexity theory is a core theoretical subject in computer science with significant impact also for real-world applications. Although a plethora of individual results are known, a systematic conceptual organisation of this knowledge is still lacking. We propose a modelling approach for creating an ontologically well-founded knowledge base for the theory of computational complexity that will enable storing, querying and reasoning over the vast knowledge of algorithmic problems, complexity classes and their relationships. We determine the core concepts and relations of complexity theory and model them on two levels of approximation: a lightweight version based on the decidable description logic S R O I Q (the underlying formalism of the ontology language OWL 2 DL) and a further extended version based on first-order logic.
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.
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.
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.
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.
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.
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.
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.
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.
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 rdflib 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.
Ontologies are a means to codify our knowledge of existence, representing concepts and their relationships. Applied ontology is the use of ontological approaches for practical purposes, including representing and solving problems within specific domains. This article retrospectively reflects on the applied ontology aspects of developing a computer-aided security modelling tool. It discusses the evolution of the ontology that underpins the modelling tool, in accordance with the incremental development of tool features to address new domain problems and feedback from concrete applications. The reflection is supported by a new OntoUML interpretation of the tool's evolving metamodel, which originally codified domain knowledge and practices. The retrospective analysis of the iterative and incremental tool development demonstrates the applied ontology aspects of a metamodel-driven development approach and provides empirical groundings of ontological unpacking, multi-level modelling, and a newly suggested technique: ontological packing. Making metamodel evolution aspects explicit highlights the significant role of metamodels and other applied ontology techniques in system development, and it suggests that they could be more systematically used for development-related decision making. A comparison with state-of-the-art ontologies further highlights aspects of applied ontology design and validation attributed to the metamodel-driven development approach.
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.
Foundational ontologies are usually developed in powerful logical languages, while they are often implemented in applications via their formalisations in the Web Ontology language (OWL). These OWL formalisations are in fact approximations of the original theories, to cope with the well-known limited expressivity of OWL. In this paper, we propose a novel modular approach to the OWL rendering of the Descriptive Ontology for Linguistic and Cognitive Engineering (dolce). We start presenting two fundamental modules of dolce in OWL 2: (i) a core module of dolce (termed 'DOLCEbasicOWL'), which provides the main taxonomy and the binary relations of the foundational ontology and (ii) an extension (termed 'DOLCEnaryRelOWL') to deal with the n-ary relations of dolce (for n > 2). We examine how the OWL rendering requires approaching delicate and truly ontological issues to motivate the choices made to circumvent the limited expressivity. To provide a minimal justification of our approximation, we establish that the OWL 2 versions are compatible with the original version of dolce by offering an automated proof that the first-order version of dolce entails the translations into first-order logic of the OWL 2 modules. Other adequacy criteria are then discussed. Finally, we illustrate the functioning of our rendering by means of examples. We conclude by discussing a number of other modules to cope with other core concepts and specific domains.
Defeasible reasoning is a kind of reasoning where some generalisations may not be valid in all circumstances, that is, general conclusions may fail in some cases. Various formalisms have been developed to model this kind of reasoning, especially characteristic of common-sense contexts. However, it is not easy for a modeller to choose a formalism that is a good fit for a particular domain from an ontological point of view. In this paper, we present a framework for formulating the characteristics of defeasibility and reasoning with exceptions which exploits and incorporates related fields of inquiry, including generics, ceteris paribus laws, and a truthmaking theory for generalisations. The resulting theory allows a grounded comparison of the various formalisms and reveals their ontological commitments. To illustrate and apply this framework, we compare the main systems of non-monotonic logics, showing the differences that may occur from an ontological perspective.
Research in the Digital Humanities requires computer systems that can document and analyze interpretations of creative works, such as literary texts. These systems are designed to provide access to existing interpretations and identify their similarities and differences. To this end, we propose an approach to formally representing interpretations. In particular, we develop an ontology of observations aimed at capturing, through what we call observational vocabularies, what interpreters claim about the texts that they interpret. We distinguish between different types of observations, in particular, basic observations, which represent specific and domain-dependent claims (e.g., regarding the analysis of literary characters' traits), and observations such as assertion, denial, support, and defeat to document more nuanced claims. We also introduce formal mechanisms that can be used to analyze particular (sets of) observations, texts, and so on. Throughout the article, we illustrate the discussion with examples from literary studies.
Dispositions are properties that can manifest under certain conditions. Their manifestations may create new conditions under which further dispositions can manifest. Thus, the manifestation and activation of dispositions have been described as making the world "tick," like a clock. Representing dispositions is essential for modeling how their bearers change through time and, therefore, for representing events. However, dispositions rarely work alone: in many cases, the changes undergone by some entity are determined by many interacting dispositions. Nevertheless, dispositions are usually characterized by stimulus-manifestation pairs. This kind of definition provides little information regarding what happens when multiple dispositions manifest simultaneously. Even in cases that we can describe with mathematical models, we lack tools to connect the mathematical and ontological models. We propose a method to represent dispositions by associating them with vectors over quality dimensions. These vectors indicate the direction and intensity of the change that a quality will undergo when the disposition manifests. The interaction between distinct dispositions is a function of their vectors. Thus, we are able to connect mathematical modeling of phenomena to an ontology that supports reasoning using inference tools. Finally, we apply our proposal in modeling oil flow inside a reservoir in the petroleum production domain.
