Purpose. The article aims to develop a formalized domain-specific language, UIDSL (User Interface Domain-Specific Language), designed to describe the logic of adaptive user interfaces, and to justify its integration with large language models (LLMs) for the automated generation, transformation, and verification of UI components within personalized digital environments. Research methods. The study employs a combination of formal, semantic, and experimental research methods. At its core lies the construction of the UIDSL grammar – a specialized language that describes the logic of adaptive interface behavior. This is achieved using formal language theory, predicate logic, and context-sensitive modeling, which together enable the formalization of adaptation conditions, UI component types, and user interaction scenarios. Results. A foundational UIDSL grammar has been developed, enabling the description of adaptive interface logic through declarative constructs. The study demonstrates the ability of LLMs to interpret UIDSL descriptions and generate corresponding UI components, including HTML/CSS fragments, behavioral scenarios, and explanatory texts. It shows that UIDSL ensures consistency, transparency, and scalability in modeling adaptive interfaces, and that its integration with LLMs opens up opportunities for automating UI development without compromising semantic precision. Originality. This is the first proposal of a formalized language UIDSL that combines principles of adaptive design, semantic modeling, and generative artificial intelligence. The article defines mechanisms for aligning UIDSL constructs with the transformational capabilities of LLMs, enabling not only the generation of interfaces but also their verification, testing, and documentation. It lays the groundwork for formally describing UI logic as a generative object within AI systems. Practical value. The developed UIDSL can be applied in educational platforms, decision-support systems, cultural institutions, and inclusive digital environments to create interfaces that adapt to user needs. Integration with LLMs reduces development costs, enables personalization, and enhances the accessibility of digital products. The language is also suitable for automated testing, documentation generation, and the creation of educational materials, making it promising for use in both academic and applied projects. Keywords: UIDSL, domain-specific language, adaptive interface, large language models (LLM), UI generation, formal grammar, semantic modeling, interface design automation, UI logic verification

Information technologies for processing, analysing, storing and exchanging digital medical data are the basis of digital transformation in the healthcare sector. Currently, the integration of medical information systems (MIS), various electronic resources and multi-level software tools into an integrated ecosystem of digital medicine is relevant. To ensure the effective use of digital medical data, cloud technologies are widely used, which provide access to programs and data with minimal use of infrastructure within a healthcare institution. The aim of the work is to develop a method for storing digital medical images for further use in the medical and diagnostic process, taking into account the peculiarities of internal information exchange processes in a healthcare institution (HCI) and to ensure the need for information interaction between two or more such institutions. Picture Archiving and Communication Systems (PACS) are at the forefront of use in the medical sector as a system focused on reducing costs, increasing productivity and speed of archiving and presenting medical images in electronic format. The proposed method consists of four stages of storing the formed image at different levels of further storage according to the DICOM standard: I stage – in the database of the information and diagnostic system, II stage – in PACS, in a long-term storage repository. To provide quality treatment, to better understand the state in dynamics, historical medical data of the patient over a long period of time is necessary. Since the capacity of the required disk space in healthcare institutions is limited, digital medical images at the III stage are transferred to long-term storage. The last stage ensures data anonymization for further use outside the HCI. The developed method for storing digital medical images contains a step-by-step description of actions using descriptive logic, which allows formalizing the entire process of storing medical information

This paper presents the design process of a composite wheel rim for a Formula Student race car. First, the design requirements for composite rims are outlined, which are driven to ensure safe operation as well as compliance with race regulations. Next, a novel methodology for the iterative design of composite wheel rims is proposed, and its steps are individually presented. The load cases were determined using data from lap time simulations and from practical experience from the operation of previous race cars. Material cards for the simulations were created by measuring the characteristics of the prepreg composites. The geometry of the rims was created by prioritizing the optimum contact with the tires. After creating the rim geometry, the composite material cards, and the simulation’s pre-processing, the layup iteration process began. In this manual iterative process, FEM simulations were run and their results were evaluated. The desired component properties were reached after 11 layup iterations. The final result is a weight reduction of 35% compared to the aluminum rims and 15% compared to the previous multi-piece CFRP rims, without a compromise in operational safety. The main novelty of the paper is the description of the iterative layup selection logic and process in detail, as well as demonstrating this on a concrete use case.

None of the first-order modal logics between $\mathsf{K}$ and $\mathsf{S5}$ under the constant domain semantics enjoys Craig interpolation or projective Beth definability, even in the language restricted to a single individual variable. It follows that the existence of a Craig interpolant for a given implication or of an explicit definition for a given predicate cannot be directly reduced to validity as in classical first-order and many other logics. Our concern here is the decidability and computational complexity of the interpolant and definition existence problems. We first consider two decidable fragments of first-order modal logic $\mathsf{S5}$: the one-variable fragment $\mathsf{Q^1S5}$ and its extension $\mathsf{S5}_{\mathcal{ALC}^u}$ that combines $\mathsf{S5}$ and the description logic$\mathcal{ALC}$ with the universal role. We prove that interpolant and definition existence in $\mathsf{Q^1S5}$ and $\mathsf{S5}_{\mathcal{ALC}^u}$ is decidable in coN2ExpTime, being 2ExpTime-hard, while uniform interpolant existence is undecidable. These results transfer to the two-variable fragment $\mathsf{FO^2}$ of classical first-order logic without equality. We also show that interpolant and definition existence in the one-variable fragment $\mathsf{Q^1K}$ of first-order modal logic $\mathsf{K}$ is non-elementary decidable, while uniform interpolant existence is again undecidable.

A logical description of priority separable games

The article substantiates the concept Bank 5.0 as a new institutional way of organizing banking in conditions of digital disintermediation. The research leans on theoretical and analytical approaches based on comparison of Bank 4.0 with the system Bank 5.0 being shaped now. The author carried out institutional analysis of changes connected with including banks in the distributed environment and gave logical and structural description of functions that are realized in digital infrastructures. It was shown that with transit to Bank 5.0 the functional role of the bank changes. It is no longer a universal intermediary that directly performs banking operations and starts acting as a participant of digital infrastructure. The bank bears responsibility for checking acceptability and correctness of operations, verification of client personality, observance of deal terms and collaboration with other components of finance ecosystem. Its functions include not only delivery of all services and provision of safe access to them, but also juridical justification of transactions and institutional coordination of digital processes. The bank becomes a junction in the distributed environment, where functions are performed through protocols, interfaces and digital identifiers, but not though centralized procedures. In this way its significance is retained not at the expense of control over operations but at the expense of participation in logics of confidence, performance and standard compliance. Research findings show the shift of bank functional role from centralized operator to a junction of digital infrastructure providing juridical competence and technical realization of operations. The transit to the concept Bank 5.0 is accompanied by institutional transformation, where bank participates not as a service supplier, but as a validating agent in the distributed system. It could require revising of basic ideas in banking and principles of digital interaction regulation.

Abstract The article discusses the reality of information for archivists in an era when information is not primarily shaped in documentary form. The situation in which contemporary administrative, business, and private information is organized in various formats necessitates a reexamination of the subject of archival science and practice. The author focuses on the ontology of the new professional standard, Records in Contexts, using it to redefine essential professional concepts and develop archival descriptions. As it relies on Description Logics, Records in Contexts introduces two new functions in addition to the existing functions. The author presents the first, terminology and discourse-related function by disassembling archival terminology into simpler, more straightforward concepts or their constituents, and analyzing them through the lenses of Description Logics. This function acknowledges today’s new reality, where information is spread in more forms than just documents. The second function is related to a Description Logics reasoning engine that supports the RiC ontology, allowing for the creation of information that does not need to be anticipated by archivists. This liberates archivists from the Schellenbergian illusion of anticipating users’ needs. The article offers an example of a practical exercise for students in archival science, showing how description is powerful when formatted as data according to the new standard. The two functions discussed are interconnected, and a comprehensive approach would have archivists manage all types of information and organize their output more semantically. The conclusion indicates such a direction, a broader application of the new archival standard compared to the previous one.

This paper introduces the Intensional Conceptualization Model for Open Environments (ICMOE), a formal framework designed to enable semantic integration in dynamic and distributed systems. Grounded in intensional logic and formalized via a domain-specific language (ICMOE-L) built on Description Logic (DL), the model distinguishes between intensional and extensional semantics, allowing structured representation and evolution of concepts, relations, and domain rules under the open world assumption. ICMOE supports advanced semantic reasoning through an interpretation function that bridges relational data and ontological structures. A formal complexity analysis shows that reasoning with ICMOE-L has a worst-case complexity of O(n) ), where n is the total number of TBox and ABox axioms. To validate its effectiveness, ICMOE is evaluated using both qualitative and quantitative metrics. The model achieves a Concept Coverage score of 0.94, Semantic Depth of 0.89, Dynamic Adaptability Index of 0.91, Semantic Rule Density of 0.85, and Ontology Alignment Efficiency of 0.88. These results demonstrate ICMOE’s superior scalability, semantic richness, and adaptability when compared to foundational models such as those by Guarino and Bealer—making it a robust solution for open distributed environments.

Tobacco (Nicotiana tabacum) production plays a crucial role in the agricultural economy of several regions around the world, especially in developing countries, such as Brazil. However, the lack of well-defined terminology and semantic models harms the development of decision support systems. Conceptualizing the tobacco production domain is challenging due to its ambiguous terminology and the complexity involved in considering environmental, soil, disease, and pest management factors. We present an Ontology of Tobacco Production (OnTop), designed to assist tobacco production in optimizing crop management practices and placing environmental factors within a formal framework. To the best of available knowledge, this study is the first to formalize the tobacco production lifecycle integrated with soil and climate data. It includes symbolic and description logic for reasoning and automation. This paper presents the proposal for the core set (main classes) and the application of OnTop to enrich a Large Language Model (LLM) to provide an ontology-based decision support prompt. OnTop allows actionable recommendations based on environmental, agronomic, and productivity data. The main contributions of this paper are: i) a domain ontology that formalizes knowledge on data-driven tobacco production; and ii) a queryable framework (enriched LLM), which allows experts to obtain complex agronomic answers. OnTop offers an extensible framework for decision making in tobacco farming and paves the way for further innovations in ontologies and LLM-based decision support systems for the agricultural domain.

The necessity to manage inconsistency in Description Logics Knowledge Bases (KBs) has come to the fore with the increasing importance gained by the Semantic Web, where information comes from different sources that constantly change their content and may contain contradictory descriptions when considered either alone or together. Classical reasoning algorithms do not handle inconsistent KBs, forcing the debugging of the KB in order to remove the inconsistency. In this paper, we exploit an existing probabilistic semantics called DISPONTE to overcome this problem and allow queries also in case of inconsistent KBs. We implemented our approach in the reasoners TRILL and BUNDLE and empirically tested the validity of our proposal. Moreover, we formally compare the presented approach to that of the repair semantics, one of the most established semantics when considering DL reasoning tasks.

Lately, the availability of massive amounts of data necessitates the adoption of modern representation techniques, such as knowledge graphs (KGs). KGs are typically constructed via automated procedures and by utilizing heterogeneous data sources. This nevertheless hinders the quality of these large resulting KGs, as they might contain contradictions, that is a set of assertions that conflict with some axioms often set by human experts. In turn, classical description logics reasoners cannot be applied as no useful inference results can be generated in the face of inconsistencies. Meanwhile, classical reasoners can be used in order to retrieve the inconsistency explanations, but as the KG size grows larger the required time for this task increases significantly. To address the problem of reasoning with large and inconsistent KGs, we propose an open-source system that detects and fixes inconsistencies by splitting the KG into modules and then processing them in parallel to speed up the process. An empirical evaluation of two datasets illustrates the potential for effective inconsistency detection and fixing of large KGs.

Abstract Since the introduction of Digital Engineering (DE) as a well‐defined concept in 2018, organizations and industry groups have been working to interpret the DE concepts to establish consistent meta‐models of those interrelated concepts for integration into their DE processes and tools. To reach the breadth and depth of DE concept definitions, the interpretation of international standard sources is necessary, including ISO/IEC/IEEE 15288, 24765, 42000‐series, 15408, 15206, 27000‐series, and 25000‐series, to effectively model the knowledge domain where digital engineering applies. The harmonization of the concepts used in these international standards continues to improve with each revision, but it may be more effectively accomplished by relying on the descriptive logic formalized in the Web Ontology Language (OWL 2 DL). This paper presents a verified and consistent ontology based on the Basic Formal Ontology (BFO) and Common Core Ontologies (CCO) that defines Seamless Digital Engineering as a digital tooling paradigm that relies on formal verification of digital interfaces to provide a system‐level qualification of the assured integrity of a Digital Engineering Environment. The present work defines classes and equivalence axioms, while using only the BFO‐ and CCO‐defined object properties that relate them, to provide a baseline analysis that may inform future DE‐related ontology development, using a case study to formally define the ‘seamless’ quality in relation to the updated ISO 25010 SQuaRE product quality model. We identified ISO meta‐model inconsistencies that are resolvable using the BFO/CCO ontological framework, and define ‘seamless’ as both a system integration quality and a Human‐Computer Interface quality‐in‐use, working to disambiguate this concept in the context of DE.

Background: Relational concept analysis (RCA) is an extension of formal concept analysis dealing with several related formal contexts simultaneously. It can learn description logic theories from data and has been used within various applications. However, RCA returns a single family of concept lattices, though, when the data feature circular dependencies, other solutions may be considered acceptable. The semantics of RCA, provided in an operational way, does not shed light on this issue. Objectives: This paper aims at defining precisely the semantics of RCA and identifying alternative solutions. Methods: We first characterise the acceptable solutions as those families of concept lattices which belong to the space determined by the initial contexts (well-formed), which cannot scale new attributes (saturated), and which refer only to concepts of the family (self-supported). We adopt a functional view on the RCA process by defining the space of well-formed solutions and two functions on that space: one expansive and the other contractive. In this context, the acceptable solutions are the common fixed points of both functions. Results: We show that RCA returns the least element of the set of acceptable solutions. In addition, it is possible to build dually an operation that generates its greatest element. The set of acceptable solutions is a complete sublattice of the interval between these two elements. Its structure, and how the defined functions traverse it, are studied in detail.

Query Answering in Lattice-based Description Logic

Fuzzy Lattice-based Description Logic

Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies are expressed using Description Logics (DLs). Initial approaches to generate embeddings relied on constructing a graph out of ontologies, neglecting the semantics of the logic therein. Recent semantic-preserving embedding methods often target lightweight DL languages such as E L + + , ignoring more expressive information in ontologies. Although some approaches aim to embed more descriptive DLs such as A L C , those methods require the existence of individuals, while many real-world ontologies are devoid of them. We propose an ontology embedding method for the A L C DL language that considers the lattice structure of concept descriptions. We use connections between DL and Category Theory to materialize the lattice structure and embed it using an order-preserving embedding method. We show that our method outperforms state-of-the-art methods in several knowledge base completion tasks. This is an extended version of our previous work, where we incorporate saturation procedures that increase the information within the constructed lattices. We make our code and data available at https://github.com/bio-ontology-research-group/catE .

Many practical applications, such as medical diagnosis, business decision-making, information searching and retrieval, etc., require usage of uncertain or ambiguous knowledge. Classical ontology-based technologies can represent and reasoning only with cri Several fuzzy or probabilistic extensions of classical description logics and languages for semantic knowledge representation have been proposed recently, but high reasoning complexity of its decision procedures make difficult its usage in real applications. It is of great importance to select the knowledge representation technology, ensuring both the needed expressiveness and effective reasoning. In this paper we make short analysis of knowledge representation and reasoning capabilities of description logics and ontology representation languages allowing representation of uncertain knowledge and propose a methodology for selecting the best description logic and ontology representation variant for every practical application.

Recently, there has been significant progress in the development of robust and highly scalable neurosymbolic description logic reasoners. However, the field faces challenges arising from diverse design strategies and evaluation methods. We address the latter challenge by emphasizing the critical requirement for a comprehensive benchmark framework tailored to the unique evaluation needs of neurosymbolic description logic reasoners. In this paper, we address barriers that must be overcome to facilitate the effective evaluation of these reasoners and outline a potential methodology for designing the benchmark framework. This work contributes toward a more systematic and principled evaluation framework for neurosymbolic reasoning, highlighting the broader role of benchmarks in advancing the field.

We introduce the class of tree constraint automata with data values in Z (equipped with the less than relation and equality predicates to constants) and we show that the nonemptiness problem is ExpTime-complete. Using an automata-based approach, we establish that the satisfiability problem for CTL(Z) (CTL with constraints in Z) is ExpTime-complete and the satisfiability problem for CTL*(Z) is 2ExpTime-complete solving a longstanding open problem (only decidability was known so far). By-product results with other concrete domains and other logics, such as description logics with concrete domains, are also briefly presented.

Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. No live animals were used. The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. VBO is an open, community-driven ontology representing over 19 500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. The adoption of VBO as a standard for breed names in databases and veterinary electronic health records enhances veterinary data interoperability and computability, supporting precision medicine.