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Control applications that facilitate Demand Flexibility (DF) are difficult to deploy at scale in existing buildings. The heterogeneity of systems and non-standard naming conventions for metadata describing data points in building automation systems often lead to ad-hoc and building-specific applications. In recent years, several researchers investigated semantic models to describe the meaning of building data. They suggest that these models can enhance the deployment of building applications, enabling data exchanges among heterogeneous sources and their portability across different buildings. However, the studies in question fail to explore these capabilities in the context of controls. This paper proposes a novel semantics-driven framework for developing and deploying portable DF control applications. The design of the framework leverages an iterative design science research methodology, evolving from evidence gathered through simulation and field demonstrations. The framework aims to decouple control applications from specific buildings and control platforms, enabling these control applications to be configured semi-automatically. This allows application developers and researchers to streamline the onboarding of new applications that could otherwise be time-consuming and resource-intensive. The framework has been validated for its capability to facilitate the deployment of control applications sharing the same codebase across diverse virtual and real buildings. The demonstration successfully tested two controls for load shifting and shedding in four virtual buildings using the Building Optimization Testing Framework (BOPTEST) and in one real building using the control platform VOLTTRON. Insights into the current limitations, benefits, and challenges of generalizable controls and semantic models are derived from the deployment efforts and outcomes to guide future research in this field.

Abstract Background The quality of Big Data analysis in medicine and surgery heavily depends on the methods used for clinical data collection, organization, and storage. The Knowledge Graph (KG) represents knowledge through a semantic model, enhancing connections between diverse and complex information. While it can improve the quality of health data collection, it has limitations that can be addressed by the Decentralized (blockchain-powered) Knowledge Graph (DKG). We report our experience in developing a DKG to organize data and knowledge in the field of emergency surgery. Methods and results The authors leveraged the cyb.ai protocol, a decentralized protocol within the Cosmos network, to develop the Emergency Surgery DKG. They populated the DKG with relevant information using publications from the World Society of Emergency Surgery (WSES) featured in the World Journal of Emergency Surgery (WJES). The result was the Decentralized Knowledge Graph (DKG) for the WSES-WJES bibliography. Conclusions Utilizing a DKG enables more effective structuring and organization of medical knowledge. This facilitates a deeper understanding of the interrelationships between various aspects of medicine and surgery, ultimately enhancing the diagnosis and treatment of different diseases. The system’s design aims to be inclusive and user-friendly, providing access to high-quality surgical knowledge for healthcare providers worldwide, regardless of their technological capabilities or geographical location. As the DKG evolves, ongoing attention to user feedback, regulatory frameworks, and ethical considerations will be critical to its long-term success and global impact in the surgical field.

Background: The quality of Big Data analysis in medicine and surgery heavily depends on the methods used for clinical data collection, organization, and storage. The Knowledge Graph (KG) represents knowledge through a semantic model, enhancing connections between diverse and complex information. While it can improve the quality of health data collection, it has limitations that can be addressed by the Decentralized (blockchain-powered) Knowledge Graph (DKG). We report our experience in developing a DKG to organize data and knowledge in the field of emergency surgery. Methods and results: The authors leveraged the cyb.ai protocol, a decentralized protocol within the Cosmos network, to develop the Emergency Surgery DKG. They populated the DKG with relevant information using publications from the World Society of Emergency Surgery (WSES) featured in the World Journal of Emergency Surgery (WJES). The result was the Decentralized Knowledge Graph (DKG) for the WSES-WJES bibliography. Conclusions: Utilizing a DKG enables more effective structuring and organization of medical knowledge. This facilitates a deeper understanding of the interrelationships between various aspects of medicine and surgery, ultimately enhancing the diagnosis and treatment of different diseases. The system's design aims to be inclusive and user-friendly, providing access to high-quality surgical knowledge for healthcare providers worldwide, regardless of their technological capabilities or geographical location. As the DKG evolves, ongoing attention to user feedback, regulatory frameworks, and ethical considerations will be critical to its long-term success and global impact in the surgical field. [ABSTRACT FROM AUTHOR]

Background: Within the scope of the , the ADVANCE project – Advanced metadata standards for biodiversity survey and monitoring data: supporting of research and conservation – aimed at supporting rich metadata generation with interoperable metadata standards and semantic artefacts that facilitate data access, integration and reuse across terrestrial, freshwater and marine realms. HMC's mission is to facilitate the discovery, access, machine-readability, and reuse of research data across and beyond the Helmholtz Association. New information: We revised, adapted and expanded existing metadata schemas, vocabularies and thesauri to build a FAIR metadata schema and a metadata entry form built on it for users to provide their metadata instances focused on biodiversity monitoring data. The schema is FAIR because it is both machine-interpretable and follows domain-relevant community standards. This report provides a general overview of the project results and instructions on how to access, re-use and complete the metadata form. [ABSTRACT FROM AUTHOR]

In the rapid development of urban construction, underground pipelines play a crucial role. However, the current underground pipelines have poor association with relevant management departments, and there are deficiencies in data completeness, accuracy, and information content. Managing and sharing information resources is relatively difficult, transforming the constructed 3D underground pipeline geographic information systems into an 'Information silo'. This results in redundant construction and resource wastage of underground utilities. The complex distribution characteristics of underground utilities make rapid batch modeling and post-model maintenance challenging. Therefore, researching a 3D spatial data fusion model for urban underground utilities becomes particularly important. Given the above problem, this paper proposes a spatial data fusion model for underground pipeline scene modeling. It elaborates on the geometric, semantic, and temporal characteristics of underground pipelines, encapsulating these features. With underground pipeline objects as the core and pipeline characteristics as the foundation, a spatial data fusion model integrating multiple characteristics of underground pipelines has been constructed. Through software development, the data model designed in this paper facilitates rapid construction of underground pipeline scenes. This further enhances the consistency and integrity of underground pipeline data, enabling shared resources and comprehensive supervision of facility operations on a daily basis. [ABSTRACT FROM AUTHOR]

Online product reviews (OPR) are a commonly used medium for consumers to communicate their experiences with products during online shopping. Previous studies have investigated the helpfulness of OPRs using frequency-based, linguistic, meta-data, readability, and reviewer attributes. In this study, we explored the impact of robust contextual word embeddings, topic, and language models in predicting the helpfulness of OPRs. In addition, the wrapper-based feature selection technique is employed to select effective subsets from each type of features. Five feature generation techniques including word2vec, FastText, Global Vectors for Word Representation (GloVe), Latent Dirichlet Allocation (LDA), and Embeddings from Language Models (ELMo), were employed. The proposed framework is evaluated on two Amazon datasets (Video games and Health & personal care). The results showed that the ELMo model outperformed the six standard baselines, including the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. In addition, ELMo achieved Mean Square Error (MSE) of 0.0887 and 0.0786 respectively on two datasets and MSE of 0.0791 and 0.0708 with the wrapper method. This results in the reduction of 1.43% and 1.63% in MSE as compared to the fine-tuned BERT model on respective datasets. However, the LDA model has a comparable performance with the fine-tuned BERT model but outperforms the other five baselines. The proposed framework demonstrated good generalization abilities by uncovering important factors of product reviews and can be evaluated on other voting platforms. [ABSTRACT FROM AUTHOR]

Twitter has emerged as a powerful Microblogging site in recent years. It has become one of the immediately reported platforms about any crisis in today's world. During a crisis twitter is flooded with both informational and non-informational tweets. Informational tweets are the tweets which can help humanitarian response organizations or the victims to get useful information for better decision making This paper proposes a Semantic based Ontology Infused deep learning model for Informative tweet classification during natural disasters. The proposed model makes use of both text and image features present in a tweet during a classification, a Hierarchical ensemble of Vanilla and Bi-LSTM's is used along with ontologies and external knowledge from OntoCollab for text classification, a dilated convolution-based residual neural network is used for image classification, and a meaningful statistical fusion is performed with the results obtained from text and image for tweet classification. The usage of both image and text features and contextual understanding made the model distinctive and achieved exceptional results. The proposed method has proven, after experiments, to achieve evaluation metrics better than the up-to-date existing methods; evaluated on seven different datasets and compared with eight other methods with an increase in Accuracy and decrease in FNR for seven distinguished datasets which resulted an highest F1 score of 98.416% on Sri Lanka Floods dataset. [ABSTRACT FROM AUTHOR]

At the domains of agriculture and livestock farming a large amount of data are produced through numerous heterogeneous sources including sensor data, weather/climate data, statistical and government data, drone/satellite imagery, video, and maps. This plethora of data can be used at precision agriculture and precision livestock farming in order to provide predictive insights in farming operations, drive real-time operational decisions, redesign business processes and support policy-making. The predictive power of the data can be further boosted if data from diverse sources are integrated and processed together, thus providing more unexplored insights. However, the exploitation and integration of data used in precision agriculture is not straightforward since they: i) cannot be easily discovered across the numerous heterogeneous sources and ii) use different structural and naming conventions hindering their interoperability. The aim of this paper is to: i) study the characteristics of data used in precision agriculture & livestock farming and ii) study the user requirements related to data modeling and processing from nine real cases at the agriculture, livestock farming and aquaculture domains and iii) propose a semantic meta-model that is based on W3C standards (DCAT, PROV-O and QB vocabulary) in order to enable the definition of metadata that facilitate the discovery, exploration, integration and accessing of data in the domain. [ABSTRACT FROM AUTHOR]

The issues of utilizing the central type to analyze the theoretical and model properties of the idea of heredity were examined in this research, taking into account both theories and the Jonsson spectrum. Finding solutions to issues related to the enriching language for the fixed Jonsson theory is associated with the problems of heredity of Jonsson theory. Another feature of Jonsson theories was described in the presented article. That is, the conclusion concerning J-non-multidimensional theories was presented in this study. The connection between J-P-stable theories and J-non-multidimensional theories was also characterired. In addition, the main result in the article was considered for the class of semantic pairs.

The work is related to the study of the model-theoretic properties of Jonsson theories, which, generally speaking, are not complete. In the article, on the Boolean of Jonsson subsets of the semantic model of some fixed Jonsson theory, the concept of the Jonsson closure operator Jcl was introduced, defining the J-pregeometry on these subsets, and some results were obtained describing this closure operator.

To explain how the human brain represents and organizes meaning, many theoretical and computational language models have been proposed over the years, varying in their underlying computational principles and in the language samples based on which they are built. However, how well they capture the neural encoding of lexical semantics remains elusive. We used representational similarity analysis (RSA) to evaluate to what extent three models of different types explained neural responses elicited by word stimuli: an External corpus‐based word2vec model, an Internal free word association model, and a Hybrid ConceptNet model. Semantic networks were constructed using word relations computed in the three models and experimental stimuli were selected through a community detection procedure. The similarity patterns between language models and neural responses were compared at the community, exemplar, and word node levels to probe the potential hierarchical semantic structure. We found that semantic relations computed with the Internal model provided the closest approximation to the patterns of neural activation, whereas the External model did not capture neural responses as well. Compared with the exemplar and the node levels, community‐level RSA demonstrated the broadest involvement of brain regions, engaging areas critical for semantic processing, including the angular gyrus, superior frontal gyrus and a large portion of the anterior temporal lobe. The findings highlight the multidimensional semantic organization in the brain which is better captured by Internal models sensitive to multiple modalities such as word association compared with External models trained on text corpora. [ABSTRACT FROM AUTHOR]

Analyzing and modeling the implicit semantic relationships in data sources is the key to achieving integration and sharing of heterogeneous data information. However, manual modeling of data semantics is a laborious and error-prone task that demands significant human effort and expertise. The paper proposes a novel explainable representation learning-based method that adopts an attention-based table-graph cross-modal retrieval model as a rating function during incremental search for automatic semantic modeling. Our supervised model utilizes the graph representation learning technique to extract latent semantics from data and aims to retrieve the most reliable semantic model for structured data sources. Experimental results demonstrate the effectiveness and robustness of our method. • A novel supervised table-graph cross-modal retrieval model for automatic semantic modeling. • An explainable representation learning method combining graph representation learning with an incremental search algorithm. • An attention mechanism and iterative matching method to learn fine-grained semantic matches between different modalities. • The experimental results using two different semantic labelers validate the effectiveness and robustness of our method. • The cross-modal retrieval-based method outperforms the previous best method in terms of precision, recall, and f1-score. [ABSTRACT FROM AUTHOR]

Objectives. Models and tools for designing adaptive user interfaces for intelligent systems are being developed. The relevance is determined by the need to reduce overhead costs and development time for user interfaces and to provide their adaptation to the specific characteristics of the user of the intelligent system.Methods. Existing approaches to designing user interfaces are being analyzed. A semantic model of an adaptive user interface for intelligent systems is proposed, implemented using a basic universal language for representing knowledge based on set theory and graph theory.Results. An adaptive user interface model for intelligent systems has been developed, which includes a knowledge base model of the user interface, an agent-oriented model of the user interface, and a library of reusable components that provide integration of the user interface into both individual intelligent systems and intelligent systems groups. A method of transferring user interface components within an intelligent systems group during the operation of an intelligent system has also been developed.Conclusion. Developed models and tools allow to simplify the reuse of user interface components together with knowledge base and problem-solving components in the design and development of individual intelligent systems, as well as in the design and development of a group of semantically compatible intelligent systems, ensuring automation of integration of user interfaces and their adaptation for each user. The developed set of user interface components has been included in a library of reusable user interface components. Further expansion of the set of components in the library and their integration into a group of semantically compatible intelligent systems is planned.

The paper proposes an approach to the individualization of user interfaces based on the integration of existing ontologies in the form of a single semantic model. The relevance of the work is due to the need to improve the efficiency and usability of user interfaces of intelligent systems. Guidelines for applying software individualization to achieve the highest possible level of usability based on ISO 9241-129:2010 and existing ontologies for implementing individualization are reviewed. A semantic model is developed to provide individualization of user interfaces of ostis-systems. The model is implemented within the framework of the Nika intelligent dialog assistant and is a reusable component, the use of which allows to reduce the cost of development of intelligent systems that require user interface individualization possibility.

Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.

In the rapid development of urban construction, underground pipelines play a crucial role. However, the current underground pipelines have poor association with relevant management departments, and there are deficiencies in data completeness, accuracy, and information content. Managing and sharing information resources is relatively difficult, transforming the constructed 3D underground pipeline geographic information systems into an ‘Information silo’. This results in redundant construction and resource wastage of underground utilities. The complex distribution characteristics of underground utilities make rapid batch modeling and post-model maintenance challenging. Therefore, researching a 3D spatial data fusion model for urban underground utilities becomes particularly important. Given the above problem, this paper proposes a spatial data fusion model for underground pipeline scene modeling. It elaborates on the geometric, semantic, and temporal characteristics of underground pipelines, encapsulating these features. With underground pipeline objects as the core and pipeline characteristics as the foundation, a spatial data fusion model integrating multiple characteristics of underground pipelines has been constructed. Through software development, the data model designed in this paper facilitates rapid construction of underground pipeline scenes. This further enhances the consistency and integrity of underground pipeline data, enabling shared resources and comprehensive supervision of facility operations on a daily basis.

Intangible cultural heritage is an important part of China's excellent traditional culture, and its inheritance and development are attached to a specific time and space. From the digital humanities perspective, the visualization of intangible cultural heritage spatiotemporal data knowledge is important for promoting the protection and inheritance of intangible cultural heritage. Research on the spatiotemporal semantic model of intangible cultural heritage can show the multiple relationships of intangible cultural heritage spaces, which is conducive to the semantic description specification and effective expression of intangible cultural heritage spatiotemporal data. [ABSTRACT FROM AUTHOR]

It is well known that amalgamation property and the joint embedding property are independent of each other. In the case when both of these properties take place within studying of inductive theories, we have a lot of classical examples from algebra that satisfy these conditions. The authors of this article raise the question of studying classical issues of Model Theory such as categoricity, completeness, syntactic and semantic similarities within the framework of the above conditions, which define a rather wide subclass of inductive theories and which are called Jonsson theories. Since any theory can be transformed into a Jonsson theory in some enrichment, in our opinion it would be interesting to study some special invariant of any model of an arbitrary signature whose essence is a Jonsson spectrum of a class of models of an arbitrary language. This article considers the issues of countable and uncountable categoricity of a class of Jonsson spectrum's cosemantic theories of an arbitrary signature's the meaning of elementary equivalence. We consider this spectrum with respect to the concept of cosemanticness, which is a generalization of elementarily equivalence in the class of inductive, generally speaking, incomplete theories. As a consequence of an obtained facts, a refinement is made of the well-known results of M. Morley, D. Saracino, and P. Lindström in the framework of studying the above concepts. Also we have introduced the notion of hybrids of classes of theories by cosemanticness and two notions of similarities which preserve many model-theoretical properties. The main result of this article is a criterium of sintactically similarity for hybrids of classes from Jonsson spectrum (Theorem 21) and as applying of this result we have obtained the existence theorem of some syntactically similar algebra for any such hybrid which we considered (Theorem 22). [ABSTRACT FROM AUTHOR]

The article is committed to the study of model-theoretic properties of stable hereditary Jonsson theories, wherein we consider Jonsson theories that retain jonssonnes for any permissible enrichment. The paper proves a generalization of stability that relates stability and classical stability for Jonsson spectrum. This paper introduces new concepts such as “existentially finite cover property” and “semantic pair”. The basic properties of e.f.c.p and semantic pairs in the class of stable perfect Jonsson spectrum are studied.

The study of syntactic and semantic properties of a first-order language, generally speaking, for incomplete theories, is one of the urgent problems of mathematical logic. In this article we study Jonsson theories, which are satisfied by most classical examples from algebra and which, generally speaking, are not complete. A new and relevant method for studying Jonson theories is to study these theories using the concepts of syntactic and semantic similarities. The most invariant concept is the concept of syntactic similarity of theories, because it preserves all the properties of the theories under consideration. The main result of this article is the fact that any perfect Jonson theory which are complete for existential sentences, is syntactically similar to some polygon theory (S-polygon, where S is a monoid). This result extends to the corresponding classes of Jonsson theories from the Jonsson spectrum of an arbitrary model of an arbitrary signature.

Currently, enhancing the efficiency of vulnerability detection and assessment remains relevant. We investigate a new approach for the detection of vulnerabilities that can be used in cyber attacks and assess their severity for further effective responses based on an analysis of exploit source codes and real-time detection of features of their implementation. The key element of this approach is an exploit source code model. In this paper, to specify the model, we systematically analyze existing source code models, approaches to source code analysis in general, and exploits in particular in order to examine their advantages, applications, and challenges. Finally, we provide an initial specification of the proposed source code model. [ABSTRACT FROM AUTHOR]

Purpose: Considering the emergence and increasing expansion of various subject domains and the lack of a valid codified thesaurus, the main aim of this study is er vorp ot a semantic model of information security based on a conceptual network for use in domain ontologies, so it is applied research. Methodology: The research method is a combination of co-word analysis, library, and Delphi methods. In the first stage, the conceptual network was extracted from 7547 scientific documents on "information security" using the co-words analysis method. These documents were indexed in the Scopus databases and WOS from 2013 to 2017. Preprocessing operations on 19648 keywords and tags were done in a completely targeted manner by using five dictionaries in information security, and three dictionaries in computer science. With a minimum cooccurrence of 5 for each word in "VOS Viewer", 207 preferred concepts were selected based on the latest version of the information security dictionary, and its conceptual network was mapped. By "Gephi", betweenness centrality, density, and clustering coefficient indices were checked. Then in the second stage, for extracting a new semantic model, used the library method. So, seven related semantic models: Security ontology, information security ontology, attack ontology, vulnerability ontology, existence - Ontosec mapping, and threat taxonomy as well as the conceptual model of information systems security in libraries. These entities, classes, subclasses, relationships between them, concepts, and examples attributed to each class and subclass were studied and examined carefully. Then, 207 conceptual network concepts were adapted to the common components of these models, and a new model was presented. Finally, in third stage, using the fuzzy Delphi technique, the consensus of experts in both fields of Knowledge and Information Science (KIS) and Computer Sciences was examined. Using SPSS and Kendall's non-parametric test, the experts' agreement coefficient about the classes and sub-classes, as well as their associated concepts, were investigated. 5 classes, 6 subclasses and also 71 concepts out of 97 common concepts with an agreement coefficient above 0.7 were obtained. Finally, confirmatory factor analysis and Smart PLS structural modeling were used to check the correctness of the relationships governing the classes and subclasses in the conceptual model. Findings: The main nodes and strong links in the conceptual network of information security include: "information security," "security," "information system," "privacy," "telecommunication," "information," "intrusion detection system," "cryptography," "cyber security," "authentication," "network," "risk," "threat," and "risk management framework." The extracted semantic model has a goodness of fitting (GOF) of 0.710 and confirms 11 semantic relationships. These relationships include: "Requires level," "Diminish," "Threatens," "Exploited by," "has Source," "Uses of," "Lead to," "Attack," "Vulnerability on," "Implemented by," and "Reduce." Also, it has 5 main classes, including "Information Asset," "Security Attribution," "Threat," "Vulnerability," and "Countermeasure." There are also 6 subclasses, which include "Threat Source," "Access Path (influence way)," "Threat Tools," and "Attack," all related to the Threat class. Additionally, there are Technological countermeasures and Organizational countermeasures, which are related to the Countermeasure class. Also, it was discovered that there are 71 attributive concepts, some of which include: Password, Smart card, User, Integrity, Hacker, Malicious code, Virus, Distributed Denial Of Service (DDOS), Risk management, Backup, Digital signature, Penetration testing, Antivirus, Firewall, and so on. Conclusion: The conceptual network and semantic model can be inferred in semantic systems and databases. This research can provide a new method for creating high-level ontologies to optimize search engines and reduce false dropping, as well as recover unwanted information. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper is to propose a semantic model for describing open source software (OSS) in a machine–human understandable format. The model is extracted to support source code reusing and revising as the two primary targets of OSS through a systematic review of related documents. Design/methodology/approach: Conducting a systematic review, all the software reusing criteria are identified and introduced to the web of data by an ontology for OSS (O4OSS). The software semantic model introduced in this paper explores OSS through triple expressions in which the O4OSS properties are predicates. Findings: This model improves the quality of web data by describing software in a structured machine–human readable profile, which is linked to the related data that was previously published on the web. Evaluating the OSS semantic model is accomplished through comparing it with previous approaches, comparing the software structured metadata with profile index of software in some well-known repositories, calculating the software retrieval rank and surveying domain experts. Originality/value: Considering context-specific information and authority levels, the proposed software model would be applicable to any open and close software. Using this model to publish software provides an infrastructure of connected meaningful data and helps developers overcome some specific challenges. By navigating software data, many questions which can be answered only through reading multiple documents can be automatically responded on the web of data. [ABSTRACT FROM AUTHOR]

In this article, strongly minimal geometries of fragment hybrids are considered. In this article, a new concept was introduced as a family of Jonsson definable subsets of the semantic model of the Jonsson theory T, denoted by JDef(CT). The classes of the Robinson spectrum and the geometry of hybrids of central types of a fixed RSp(A) are considered. Using the construction of a central type for theories from the Robinson spectrum, we formulate and prove results for hybrids of Jonsson theories. A criterion for the uncountable categoricity of a hereditary hybrid of Jonsson theories is proved in the language of central types. The results obtained can be useful for continuing research on various Jonsson theories, in particular, for hybrids of Jonsson theories.

Background. The subject of the study is information on the classification and performance characteristics of military ground equipment, which is in the public domain. The object of research is ontological models of knowledge. The purpose of the work is to develop an ontology of military ground equipment. Materials and methods. The design of the ontology was based on a system analysis using the “top to bottom” method. To develop the ontology, the free software Protégé and the ontology description language OWL were used, HermiT was used as an inference machine. Results. An ontology of military ground equipment has been developed. The classes, properties and axioms that define the logic of inferences for ontological engineering of knowledge are described. Conclusions. The presented ontology can be used to solve problems in tactical and strategic planning in military affairs, as well as for educational purposes.

Theoretical foundations of GDP dynamics under conditions of market uncertainty and the formation of a digital economy are studied. Forecasting GDP is important because it allows to ensure balance, sustainability in the development of various sectors of the national economy, and its economic security, which determines the practical significance of the study. Purpose. To put forward and prove the hypothesis that with the help of the neural network model “perceptron” and the semantic model of knowledge representation, it is possible to obtain a forecast of Russia’s GDP for the next year. The scientific novelty lies in the fact that forecasts of the RF GDP were generated using a semantic knowledge representation model and the Perceptron AI system. Method or methodology of the work. The work used such methods as monographic, analytical, artificial intelligence system - perceptron, Mind histogram, as well as comparative analysis. Calculations were performed in tables XL perceptron was formed on the Deductor platform. We used a semantic knowledge representation model developed in the DOT language using the Graphviz 2.38 program. Results. Theoretical foundations of the use of neural networks in economic forecasts have been studied. A semantic model of knowledge representation has been formed regarding forecasting the GDP of the Russian Federation by a neural network. The developed AI-system “perceptron” is presented, designed to forecast Russia’s GDP based on input parameters representing a set of data reflecting the development of the real sector of the economy and the financial sector of Russia for 2011–2021. The scope of the results: economics, the financial sector, forecasting and planning of economic and financial activities.

Model-based design has become the predominant approach to the design of hybrid and cyber-physical systems (CPSs). It advocates the use of mathematically founded models to capture heterogeneous digital and analog behaviours from domain-specific formalisms, allowing all engineering tasks of verification, code synthesis, and validation to be performed within a single semantic body. Guaranteeing the consistency among the different views and heterogeneous models of a system at different levels of abstraction, however, poses significant challenges. To address these issues, Hoare and He’s Unifying Theories of Programming (UTP) proposes a calculus to capture domain-specific programming and modelling paradigms into a unified semantic framework. Our goal is to extend UTP to form a semantic foundation for CPS design. Higher-order UTP (HUTP) is a conservative extension to Hoare and He’s theory that supports the specification of discrete, realtime, and continuous dynamics, concurrency and communication, and higher-order quantification. Within HUTP, we define a calculus of normal hybrid designs to model, analyse, compose, refine, and verify heterogeneous hybrid system models. In addition, we define respective formal semantics for Hybrid Communicating Sequential Processes and Simulink using HUTP. [ABSTRACT FROM AUTHOR]

Knowledge management methods and their efficient implementation across the organization determine sound and resilient management of processes. This paper studies the semantic integration of enterprise data sources essential to service management processes. Implementing a semantic layer within the enterprise architecture uses various tools, methods, and techniques. The semantic conceptual model unifies and implements intelligent integration of multiple data sources across the enterprise, achieving consistency and more accessible interpretation. Specifically, we draw our attention to incident and problem management within enterprises. We propose an ontology -- a conceptual model for the incident management process. The incident ontology presented as an intelligent data integration layer component aims to achieve operational excellence. Besides, this ontology is a fundamental part of the proactive process in problem management. An ontology as a logic-based system supports integrity validation. It infers new, no explicitly modeled facts in the problem domain, thus helping experts better analyze and understand the problem. We discuss the conducted experiment results with the proposed in this article conceptual model using the enterprise knowledge graph platform. It can be perceived as a framework for a query-answering system with components, including ontology schema, data mapping, and classification methods for data graph enrichment. [ABSTRACT FROM AUTHOR]

В статье проводится лексико-семантический анализ гидронимов севера Забайкальского края эвенкийского происхождения. Цель данной работы – дать характеристику эвенкийскому гидронимическому материалу северных районов Забайкальского края с точки зрения принципов номинации водных объектов. Основными методами исследования являются методы этимологического, формантного, лексико-семантического анализов. Общее количество гидролексем, подвергнутых анализу, составило 246 единиц, большая часть которых представляет собой наименования рек, озёр, ручьёв. По результатам исследования определены наиболее продуктивные семантические типы и входящие в них семантические модели гидронимной лексики. Результаты актуальны для сопоставительных топонимических исследований.

A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known. [ABSTRACT FROM AUTHOR]