This paper introduces a method for evaluating the semantic fidelity of transformations between conceptual metamodels. Using a semi-formal system, we define FLAT method for quantification of enforcment, loss, alteration, and preservation (translation) of meaning through formal metrics. The method is validated by evaluating mappings from an Association-Oriented Metamodel (AOM) to UML, EER, and ORM, demonstrating its effectiveness in identifying semantic discrepancies.

The formal representation of legal norms remains a major challenge for semantic legal information systems, particularly with respect to the explicit modeling of the scope of applicability and deontic modality. Existing approaches to legal knowledge representation, including ontology-based standards such as OWL, LKIF-Core, and LegalRuleML, provide valuable conceptual foundations but lack a uniform and explicit treatment of normative scope across subject, object, spatial, temporal, and situational dimensions. As a result, reconstructing normative structures from legal texts and applying them to concrete legal situations remains difficult and error-prone. This paper introduces a semantic metamodel for legal norm representation that explicitly captures normative scope and deontic modality as first-class semantic constructs. The proposed metamodel defines a set of formal categories for normative elements, including conditions of applicability, scope constraints, normative effects, and deontic operators, while maintaining interoperability with external domain ontologies. By separating normative structure from domain-specific knowledge, the model enables consistent interpretation and reuse of legal norms across different application contexts.

The paper introduces a method for quantitatively analyzing the expressive power and semantic capacity of metamodels in the context of conceptual data modeling. The method leverages a formal semantic representation of metamodels, based on the Conceptual Layer of Metamodels (CLoM) and expressed using an ontological system of concepts. By extracting semantic constructs, such as concepts, semantic atoms, and semantic particles, the approach allows for a structured evaluation of metamodels’ abilities to express semantic complexity. The proposed method is applied to selected conceptual metamodels, including AOM, EER, ORM, and a reference graph metamodel, providing insights into their semantic capabilities. Results demonstrate alignment with expert intuition and reveal differences in semantic richness and complexity.

This paper reviews state-of-the-art methods for UAV navigation in GNSS-denied environments, focusing on map-based spatial matching and vision-based techniques. It identifies trends and challenges in current approaches, emphasizing the integration of cartographic data, visual odometry, and data fusion. A novel methodology for identifying research gaps is presented, combining systematic literature review, qualitative and quantitative analysis with multi-criteria evaluation. The findings highlight limitations in existing methods and propose a navigation system that leverages spatial pattern matching and object recognition, integrating vision-based and GIS data. This approach addresses real-time processing challenges and offers a foundation for improving UAV navigation accuracy in GNSS-denied settings.

The accurate representation of association relationships remains a fundamental concern in conceptual modeling. While most modeling languages provide constructs for associations, they differ substantially in terms of semantic richness, structural granularity, and ontological commitments. This paper investigates the semantic complexity of named (associative) relationships by analyzing selected features of association modeling across a range of conceptual modeling languages and metamodels, including UML, EER, RDF Schema, ORM, and AOM. To facilitate a uniform and semantically grounded analysis, we introduce the Conceptual Layer of Metamodels (CLoM) (Conceptual Layer of Metamodels), an ontology-driven abstraction for characterizing the semantic primitives underlying association constructs. Each modeling feature is evaluated in terms of its ontological status within CLoM, allowing us to isolate conceptual capabilities and limitations of individual metamodels. The core contribution of this work is a comparative matrix capturing the presence and interpretation of key semantic features—such as arity, identity, role inheritance, multiplicity, and role constraints—across selected modeling approaches. The paper concludes with a discussion on the semantic expressiveness of the examined metamodels and highlights potential directions for improving the ontological robustness of association modeling.

The article concerns database issues, including the model of physical data composition for self-organising data structures. It is assumed that these structures will be used to represent the k-multiset and store large numbers of B+ trees in non-volatile memory. The work presents the results achieved as part of a research project on the definition of the database metamodel. It is dedicated to the issues of modelling structures based on very elementary concepts, i.e. sets of tuples storing data and structures storing the connections between these tuples.

Air pollution is a significant public health and environmental concern that requires accurate prediction and monitoring. This paper introduces a framework that establishes a city-wide abstraction layer for air pollution prediction. The authors present contemporary advancements in air pollution modeling, including research approaches and technologies. The framework promotes a streamlined learning process and improves efficiency by generating a simulated representation of the Earth’s surface for air pollution forecasting using the Land-Use Regression (LUR) model and facilitating data visualization. The authors aim to establish a platform for exchanging research experiences and replicating findings to improve air pollution prediction and control. The framework can help policymakers, researchers, and environmentalists monitor air pollution levels and develop effective strategies to mitigate its adverse effects.

Agent-based systems follow similar rules as object-oriented models. However, they distinguish agents as entities that can perceive their environment, process information about it, change it via actions, and interact with other agents. In this paper, the authors proposed some solutions coupled to creating models of such environments on a high level of abstraction, considering the applicability of these models in real-world projects. As a result, a conceptual environment model has been elaborated and proposed. The presented case study of the AriaDNA Life system verifies the model.

The article presents a proposal of the formal symbolic notation operate for basic mathematical concepts. This notation is distinguished by its unambiguousness in terms of key terms used in computer science. This notation defines many concepts such as entity, type, structure definition, compositional relationship and aggregation relationship, references, features related to the admission of variation, ordering, uniqueness, generalisation-specialisation relationship, inheritance, and polymorphism mechanism. Purely mathematical notation operates on concepts that are blurry in the sense of computer science. Moreover, in mathematics, there are no direct mechanisms such as inheritance or polymorphism. They can be defined mathematically in many ways, which can sometimes make it difficult to understand such formalisms in the context of computer science solutions. The proposed solution is a response to a research problem defined in this way. The article also presents a case study illustrating how to apply the proposed method.

In this paper, the authors introduce a novel approach to data metamodel conceptualization called Conceptual Layer of Metamodels. This conceptualization method gives an common conceptual layer based on Semantics of Business Vocabulary and Rules for expressing data metamodels built using different categories and having other characteristics but representing similar semantics. This paper describes abstraction and concretization dependency and covers the metamodeling layer and core modeling concepts layers.

In this paper, we describe the method for expressing the semantics of data metamodels using a concept system. The method abstract from metamodels' syntax and deems to enable the modeler to compare different data metamodels, and express semantics-aware translations between different metamodels. The method is based on the Semantics Of Business Vocabulary And Rules standard. Moreover, the paper describes the Association-Oriented Metamodel as a case study for the method.

The paper is related to modeling and metamodeling disciplines, which are applicable in the software engineering domain. It is focused on the subject of finding the way leading to the selection of the right metamodel for a particular modeling problem. The approach introduced in the paper is based on a specific application of the Extended Graph Generalization, which is used to identify features of known metamodels in relation to the extensions and generalizations introduced by the Extended Graph Generalization definition. The discussion is related to an illustrative case-study. The paper introduces the Extended Graph Generalization definitions in Association-Oriented Metamodel, the Extended Graph Generalization symbolic notation, which are used when comparing features of different metamodels in relation to the Extended Graph Generalization features.

The paper presents a case study of modelling real-case industrial scenario of a grain-trading Polish company with Unified Process Metamodel (UPM). UPM is a novel approach for continuous process modelling. Many industrial processes, such as grain storage, are hard to describe by the use of conventional tools. With this example the authors emphasize the benefits of continuous process modeling paradigm over the token-based methodology and show the facilities of storage modeling and behavioral annotation in the UPM.

The subject of the paper is connected to defining data structures, which are or can be used in metamodeling and modeling disciplines. A new and general notion of Extended Graph Generalization has been introduced. This notion enables to represent arbitrarily complex such the structures. A way of introducing constraints, which allows to reduce this general form to any well known structure has been introduced as well. As the result of the extension and generalization mechanisms applied to the original graph definition any form of graph generalization exceeding well-known structures can be defined. Moreover, the way of associating any form of data to each such structure has been defined. Notions introduced in the paper are intended to be used while defining novel family of metamodels.

Research in the field of collective intelligence is currently focused mainly on determining ways to provide a more and more accurate prediction. However, the development of collective intelligence requires a more formal approach. Thus the natural next step is to introduce the formal model of collective. Many scientists seem to see this need, but available solutions usually focus on narrow specialization. The problems within the scope of collective intelligence field typically require complex models. Sometimes more than one model has to be used. This paper addresses both issues. Authors introduce graph-based meta-model of collective that intend to describe all collective's properties based on psychological knowledge, especially on Surowiecki's work. Moreover, we introduced the taxonomy of metrics that allow assessing the qualitative aspects of crowd's structure and dynamics.

Knowledge representation is one of the most explored areas in nowadays computer science research. In this paper authors pursue definition and semantics of semantic networks that are defined as part of Semantic Knowledge Base being a hybrid knowledge oriented system. The approach presented in here aims at introducing advanced properties of networks such as cardinality, partitioning or certainty at the same time using simple structure based on two operands and operators. Following paper is an extension of a conference publication that introduced advanced aspects of semantic networks modelling with the use of Association-Oriented Metamodel. The extension includes a discussion related to the formal description of the structure, as well as the description and use of association-oriented design patterns.

The monitoring of manufacturing processes is an important issue in nowadays ERP systems. One of the most important issues is to identify and analyze appropriate data for each of the production units taking part in the process. In the paper authors introduce a new approach towards modelling the relation between production units, signals and factors possible to obtain from the production system. The main idea for the system is based on the ontology of production units. The design of the system using advanced knowledge engineering is elaborated. Since, the implementation of proposed system was one of key assumptions, the relational model is presented that ensures possibility to deploy the system in the future.

This paper addresses the problem of knowledge representation from the perspective of value ranges. The subjects introduced and the problems discussed constitute a part of the Flowing Resource module under the Unified Process Metamodel (UPM) project. Resources modeling is a crucial issue in the aspect of non-token-based process definition. This in turn plays an important role in sharing information among agent constituting collective users of a given process. From a practical point of view, value ranges are very frequent elements used in resource description. The sphere of time ranges, relative, absolute, cyclic, and fuzzy has been elaborated particularly extensively, since they affect the work-availability. The author has commented upon the specificity of different units of time and their mutual relations as well as options of embedding time range-related expressions. Another topic raised in the paper is the fuzziness of values defining cyclic value ranges. For the aforementioned problems, the author has proposed solutions formally written in an Association-Oriented Database Metamodel (AODB) being database layer for UPM. Both data structures and their comprehensive semantics have been described. Moreover, four operators, whose operands are value ranges, have been discussed along with the corresponding data processing algorithms.

The paper presents the relationship module (RM) being a part of the semantic knowledge base used for the efficient semantic modeling of relations between concepts. The main contribution of presented solutions represents an approach based on the hypergraph-based data model expressed upon the association-oriented metamodel. The RM has been designed in accordance with a number of assumptions related to the representation of relationships, such as defining types of relations in terms of their characteristics, arity and hierarchy, transferring attribute among related concepts, instancing specific relationships, and determining potentially permitted or prohibited connections between concepts.

The paper is focused on comparison of two different approaches to graph modeling. A case study, which was chosen as a common background for the comparison, is an example of modeling problems characteristic for bioinformatics. First the OMG Semantics of Business Vocabulary and Rules (SBVR) standard was used to define the domain. The Association-Oriented Database (AODB) metamodel based approach was applied to show how the appropriate domain-specific model can be created in a graph modeling language dedicated to data modeling. In contrast, the Context-Driven Meta-Modeling (CDMM) approach, illustrates how to construct a domain-specific modeling language to model the case-study as a domain. The AODB is used as a General-Purpose Modeling Language (GPML) while the CDMM is applied as a Domain-Specific Modeling Language (DSML). Both approaches constitute alternatives for MOF based languages known from OMG standards.