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When designing data-driven web applications, users’ informative needs are aligned to knowledge organization (KO) requirements, which are secondly mapped to user interfaces (UI) components, and finally to user experience (UX) journeys. Particularly, when data are served as Linked Open Data, data and user requirements can be associated with competency questions that an ontology should address. However, to the best of our knowledge, there is no full-fledged methodology that systematically adopts ontology requirements to design UI components and UX journeys. In this article we propose a methodology to design web applications for information seeking purposes that leverages well-known ontology design methodologies and UI/UX approaches. We present a case study based on music heritage and we evaluate it via a user study.

Objective With its powerful semantic processing and open organization capabilities, the knowledge graph lays the foundation for knowledge-based organization and intelligent applications in various fields. In order to improve the digitization and intelligence level of urban rail transit train maintenance, a knowledge graph for the train maintenance field should be established. Method The knowledge graph technology is briefly introduced, and a top-down approach is adopted to construct the knowledge graph. The construction process of the knowledge graph for the train maintenance field is analyzed from five aspects, i.e. ontology design, information extraction, knowledge mapping, knowledge storage, and knowledge fusion. The outlook for the application of the knowledge graph in the field of train maintenance is described. Result & Conclusion Based on the above graph, not only can the problems of train maintenance data silos be effectively solved, but also the unified management of relevant data in the field of train maintenance can be realized, and intelligent services such as ′one file for one train′, intelligent retrieval, and train fault trend analysis be provided for train maintenance.

[Objective] With its powerful semantic processing and open organization capabilities, the knowledge graph lays the foundation for knowledge-based organization and intelligent applications in various fields. In order to improve the digitization and intelligence level of urban rail transit train maintenance, a knowledge graph for the train maintenance field should be established. [Method] The knowledge graph technology is briefly introduced, and a top-down approach is adopted to construct the knowledge graph. The construction process of the knowledge graph for the train maintenance field is analyzed from five aspects, i. e. ontology design, information extraction, knowledge mapping, knowledge storage, and knowledge fusion. The outlook for the application of the knowledge graph in the field of train maintenance is described. [Result & Conclusion] Based on the above graph, not only can the problems of train maintenance data silos be effectively solved, but also the unified management of relevant data in the field of train maintenance can be realized, and intelligent services such as 'one file for one train', intelligent retrieval, and train fault trend analysis be provided for train maintenance. [ABSTRACT FROM AUTHOR]

IntroductionMobile app development has rapidly evolved into a crucial aspect of modern technology, driving innovation across various industries and transforming user experiences globally. The dynamic nature of mobile technology requires developers to navigate a complex landscape of platforms, devices, and user requirements. Effective management and sharing of knowledge are essential to address these challenges, ensuring streamlined development processes and enhanced collaboration among stakeholders.MethodsTo this end, ontologies have emerged as powerful tools for structuring and standardizing domain-specific knowledge. This paper introduces MAD-onto, a comprehensive ontology designed specifically for the mobile app development domain. The ontology is constructed by identifying key concepts, defining classes and their hierarchies, establishing class properties, and creating instances relevant to mobile app development. To ensure robustness, the ontology is evaluated using a multi-criteria evaluation metric, focusing on consistency, completeness, conciseness, expandability, and sensitiveness. Additionally, SWRL rules are applied to validate and enforce logical constraints within the ontology.ResultsThrough these rigorous evaluation methods, MAD-onto demonstrates its utility in providing a structured framework for the mobile app development lifecycle, facilitating better decision-making, collaboration, and efficiency.DiscussionThe findings highlight the significance of ontology-driven approaches in addressing the complexities of mobile app development and set a foundation for future research and advancements in this field.

Using tools describe clearly and accurately the steps, components and relationships between components of phenomena graphically or hierarchically is one of the conventional methods of education. Ontology is a branch of philosophy for studying the nature of existence and give us a general picture of existence. In this article, by evaluation of some ontology designing software and selection of the best, we tried to provide a clear, formal and accurate description of the evidence-based medical field and the relationships between its components. Therefore, the main goal of this research is to identify the strengths and weaknesses of the ontology designing software and their functionality in Persian language by using the prototype design of ontology in the field of evidence-based medicine. This article is an applied research with library research and content analysis methods used to collect data. To identify ontology design software, a list of ontology design software was prepared by searching Google and Science Direct document databases, World Wide Web Consortium website, Wikipedia, Mohed’s article and various websites. 151 software were found, some were not restored and removed from the list. In the final review, 32 software were examined to select the most used and easiest software. Kogoi, Swap, Obo-Editor, Apollo and Protégé were selected as examples for the review. All of the softwares were installed, evaluated by reading the software training articles, the software guidebook and the reviews about and use of the software itself. The five investigated software were compared and by choosing the most suitable software, evidence-based medicine ontology was designed. In the review of the software, all programs are free to download and were written under Java program. In terms of learning and using of software, Protege program, Obo-Editor and Kogoi are simpler and less complicated and have useful resources to learn the software. Protege and Kogoi have better features and support, and their use was associated with fewer problems, especially in the design of ontology in Persian language. Since Protege software is more compatible with the standards of ontology design, this software was selected for the design of evidence- based medical ontology and was designed through 7 steps. Considering that this ontology is a prototype, all its classes and relationships should be added. It is proposed to develop this ontology for future research. [ABSTRACT FROM AUTHOR]

Traditional martial arts are treasures of humanity's knowledge and critical carriers of sociocultural memories throughout history. However, such treasured practices have encountered various challenges in knowledge transmission and now feature many entries on the UNESCO list of intangible cultural heritage. In tackling the urgency of knowledge preservation through digital means, this project employs an ontology-based approach to model the conceptual realm of traditional martial arts. Accordingly, it creates the Martial Art Ontology (MAon) , a comprehensive domain ontology with an annotated data resource incorporating entities and relations from embodied, epistemic, and sociocultural facets. MAon underlines the significance of embodied qualities and addresses relevant dimensions, such as kinesthetics, techniques, mnemonics, and tactics, along with stylistic, interpretative, and ideological components. It features scholarly terminology developed through literature analysis, interviews with masters, and expert validations. The instantiation of MAon is realized through annotating three archetypal Southern Chinese styles, offering exhaustive descriptions concerning techniques, forms, principles, and form sets, amongst others. In summary, the reported approach encodes the manifold of martial arts into a structured vocabulary and an interlinked data resource, accessible to both human-reading and machine-operating applications. By applying it to manifest a range of knowledge concepts, we demonstrate the potential of ontology-based datafication to create coherent representations for intangible cultural entities and to enable an interoperable data infrastructure across multimodal cultural archives. [ABSTRACT FROM AUTHOR]

The problem of constructing a knowledge graph for previously unprocessed scientific texts is studied. The task is to analyze the use of various machine processing methods for extracting text semantics using the example of an interdisciplinary subject area. The goal of the research is to create a semantic model of the subject area of an interdisciplinary scientific journal and use a knowledge graph to navigate through data. The semantic model is based on the ontology of the LibMeta library. Texts and metadata receive connections due to the processing procedure and become part of the ontology of the semantic library. The interdisciplinary subject area is integrated into the library content as a related area of the "mathematics" ontology of the LibMeta library. A comparison of texts is carried out according to different characteristics, thematic proximity, commonality of research methods and approaches, problem setting and their symbolic representation. The results of preliminary text processing and the results in the form of a knowledge graph integrated into the library content are presented. The formulation of the problem of constructing an ontology and a knowledge graph for an interdisciplinary subject area adjacent to mathematics is considered by the authors as part of the general problem of managing mathematical knowledge. [ABSTRACT FROM AUTHOR]

Large complex components are the core structural components of equipment in the fields of aerospace, energy, ships, etc. Such components have large sizes, complex shapes, and high processing accuracy requirements. Nowadays, robotized processing systems can overcome the shortcomings of high cost, poor flexibility of the gantry equipment, which have become a powerful tool for processing large complex components. However, the ontology performance of the existing robotized processing system still has problems such as small workspace and weak load capacity, so a hybrid mechanism that can be used as the core ontology of a large complex component processing robot is proposed in this study. The hybrid mechanism consists of a series of manipulators for processing tasks and a 6-UPS parallel mechanism. The 6-UPS parallel mechanism, driven by six drive rods, is used to support the series manipulator and regulate its position and pose on the one hand, and drive the robot to travel with the hybrid mechanism as the ontology on the other hand. The kinematics modeling and workspace solutions of the serial manipulator, 6-UPS parallel mechanism and the whole hybrid mechanism are completed in this study and it is proved that the workspace of the whole hybrid mechanism is significantly larger than that of the serial manipulator and the 6-UPS parallel mechanism. The virtual prototype of the robot is designed with the proposed hybrid mechanism as the robot ontology. By realizing the gait walking of the virtual prototype in a three-dimensional software, it is proved that the designed robot can further expand the workspace using a walking function.

Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it. [ABSTRACT FROM AUTHOR]

Drought disasters have caused serious impacts on the social economy and ecological environment, which are continuously and increasingly exacerbated by climate warming and other factors. Drought disaster management usually involves processing a mass of isolated data from many fields expressed in different terminologies and formats. These heterogeneous data or so-called data silos have greatly hindered drought disaster management in an information-rich manner. Establishing a drought disaster knowledge graph can facilitate the reuse of these heterogeneous data and provide references for drought disaster management, and ontology design and named entity recognition are the two major challenges. Therefore, in this study, we first designed a drought disaster ontology by recognizing the major concepts in the drought disaster field and their relationships, which was implemented with an ontology modeling language. We next constructed a drought disaster corpus and an integrated entity recognition model that was built by integrating multiple deep learning methods. Finally, we applied the integrated entity recognition model to extract information from the CNKI literature database. The integrated model shows satisfactory results in drought disaster named entity recognition. We thus conclude that combining ontology and deep learning technology toward establishing a knowledge graph for drought disasters is promising. HIGHLIGHTS Ontology was used to construct the schema for drought disaster knowledge graphs.; A corpus of drought disasters was constructed with unstructured documents.; Automatic drought disaster named entity recognition was achieved by the deep learning method.;

The tourism and hospitality sectors have become increasingly important in the last few years and the companies operating in this field are constantly challenged with providing new innovative services. At the same time, (big-) data has become the “new oil” of this century and Knowledge Graphs are emerging as the most natural way to collect, refine, and structure this heterogeneous information. In this paper, we present a methodology for semi-automatic generating a Tourism Knowledge Graph (TKG), which can be used for supporting a variety of intelligent services in this space, and a new ontology for modelling this domain, the Tourism Analytics Ontology (TAO). Our approach processes and integrates data from Booking.com, Airbnb, DBpedia, and GeoNames. Due to its modular structure, it can be easily extended to include new data sources or to apply new enrichment and refinement functions. We report a comprehensive evaluation of the functional, logical, and structural dimensions of TKG and TAO.

Ontologies can provide a valuable role in the work of cancer registration, particularly as a tool for managing and navigating the various classification systems and coding rules. Further advantages accrue from the ability to formalise the coding rule base using description logics and thereby benefit from the associated automatic reasoning functionality. Drawing from earlier work that showed the viability of applying ontologies in the data validation tasks of cancer registries, an ontology was created using a modular approach to handle the specific checks for childhood cancers. The ontology was able to handle successfully the various inter-variable checks using the axiomatic constructs of the web ontology language. Application of an ontological approach for data validation can greatly simplify the maintenance of the coding rules and facilitate the federation of any centralised validation process to the local level. It also provides an improved means of visualising the rule interdependencies from different perspectives. Performance of the automatic reasoning process can be a limiting issue for very large datasets and will be a focus for future work. Results are provided showing how the ontology is able to validate cancer case records typical for childhood tumours. [ABSTRACT FROM AUTHOR]

An ontological approach to the construction of a thesaurus of the applied subject area of a scientific academic journal based on the content of the LibMeta semantic digital library and subject area sources is considered. A thesaurus and a knowledge graph of the subject area of scientific publications of the journal of the Russian Academy of Sciences Mechanics of Composite Materials and Structures, devoted to the modeling of composite materials and structures, is being built. The approach is based on the use of the content of the semantic library LibMeta, classical primary sources on the theory of elasticity and metadata of the journal articles. As a result, a thesaurus and a knowledge graph of the subject area of the journal are built, and on their basis the user interface is modified and a variant of navigation through the links of articles is proposed. Examples of constructions are given. [ABSTRACT FROM AUTHOR]

The paper considers an approach to a knowledge graph construction based on the ontological representation of scientific subject areas. The presentation is based on concepts related to information and data mining, such as knowledge, knowledge extraction, domain ontology, scientific domain, thesaurus, semantic digital library, user information need, ontological design method and, in fact, the knowledge graph. The digital semantic library LibMeta is presented as a repository of various structured data with the possibility of their integration with other data sources. Assumes the possibility of specifying personal content by describing a local subject area within LibMeta. The ontology of the content of the semantic library acts as a means of formalization. This paper addresses the experience of building semantic libraries based on thesauri and ontological design. Building ontologies based on the thesaurus of the subject area LibMeta allows us to say that the presence of internal semantic links ensures the consistency and reliability of search results, which is a necessary condition for extracting scientific knowledge. The digital library ontology defines the data structure of the library content. Each data element loaded into the library can be associated with an ontology vertex (top) that determines the position of the data element in the ontology. Based on the ontology links and the links defined at the design stage, you can build a data graph. On the example of the ontology of the LibMeta semantic library, the technology of forming the knowledge graph of modern applications in mathematics is discussed. The problems of filling a graph, embedding in a graph, extracting links and nodes of a graph are discussed. [ABSTRACT FROM AUTHOR]

Abstract As the State Grid Multi-cloud IoT platform grows and improves, an increasing number of IoT applications generate massive amounts of data every day. To meet the demands of intelligent management of State Grid equipment, we proposed a scheme for constructing the defect knowledge graph of power equipment based on multi-cloud. The scheme is based on the State Grid Multi-cloud IoT architecture and adheres to the design specifications of the State Grid SG-EA technical architecture. This scheme employs ontology design based on a fusion algorithm and proposes a knowledge graph reasoning method named GRULR based on logic rules to achieve a consistent and shareable model. The model can be deployed on multiple clouds independently, increasing the system’s flexibility, robustness, and security. The GRULR method is designed with two independent components, Reasoning Evaluator and Rule Miner, that can be deployed in different clouds to adapt to the State Grid Multi-cloud IoT architecture. By sharing high-quality rules across multiple clouds, this method can avoid vendor locking and perform iterative updates. Finally, the experiment demonstrates that the GRULR method performs well in large-scale knowledge graphs and can complete the reasoning task of the defect knowledge graph efficiently.

Ontologies can provide a valuable role in the work of cancer registration, particularly as a tool for managing and navigating the various classification systems and coding rules. Further advantages accrue from the ability to formalise the coding rule base using description logics and thereby benefit from the associated automatic reasoning functionality. Drawing from earlier work that showed the viability of applying ontologies in the data validation tasks of cancer registries, an ontology was created using a modular approach to handle the specific checks for childhood cancers. The ontology was able to handle successfully the various inter-variable checks using the axiomatic constructs of the web ontology language. Application of an ontological approach for data validation can greatly simplify the maintenance of the coding rules and facilitate the federation of any centralised validation process to the local level. It also provides an improved means of visualising the rule interdependencies from different perspectives. Performance of the automatic reasoning process can be a limiting issue for very large datasets and will be a focus for future work. Results are provided showing how the ontology is able to validate cancer case records typical for childhood tumours.

The article focus on problem of developing a semantic library by adding a new applied scientific field. The addition to the main content of the library is built using publications of a journal on applied issues of composite materials. The description of the original subject area is expanded. Universal Decimal Classification and Mathematics Subject Classification articles are detailed by corresponding to the local subject area. At the same time, the tasks of adding terms to the thesaurus, building a reference corpus of the applied subject area of mathematics, and creating a custom interface are solved. Formulas and equations of the local subject area are semantically linked to the main content of the library. The main advantage of using semantic libraries for this kind of tasks is to enrich the existing knowledge base of the library and identify relationships in the data. To study these problems, it is necessary to interact with subject matter experts and use modern tools and methods for natural language processing, machine learning, and approaches to knowledge representation. Representation of knowledge in the form of ontologies and thesauri connects the definitions of key concepts of the subject area not only within the same scientific school, but also allows experts and users to "communicate" in the same language. As a global thesaurus of the subject area, terminologically delineating its boundaries, the data of the Mathematical Encyclopedia, a Soviet encyclopedic edition integrated into the library, are used. Integration of data within the library allows expanding the description of subject areas related to the applications of mathematics in interdisciplinary research and technology. On the example of one of the applied sections of problems of mathematical physics, the procedure is shown for including arrays of publications of a specialized journal into the ontology of a semantic library. The ontology is based on available data sources, library content, and specific dictionaries and thesauri. The proposed approach will allow using the content of the semantic library "Mathematics" for scientific research, minimizing the process of searching for information in the local subject area, without losing more general results contained outside this area. [ABSTRACT FROM AUTHOR]

The security and stability control system is the guarantee of the security and stability operation of the power grid. With the increasing scale of distributed new energy access to the power grid, the security and stability control strategy of the power grid is becoming more complex, and it is becoming increasingly important to correctly analyze and implement the security and stability control strategy. In order to ensure the correctness of the security and stability control strategy implemented by the security and stability control device, it is necessary to analyze the security and stability control strategy in detail. Therefore, this article proposes an intelligent analysis method of the security and stability control strategy based on the knowledge graph. First, this article introduces the ontology design method of the security and stability control strategy based on the knowledge graph, combines the characteristics and applications of the knowledge graph, analyzes the relationship between the elements of the strategy, and designs a clear-structured knowledge network. Second, this article analyzes the automatic construction technology of the graph, constructs the six-element ontology model of the security and stability control strategy, and realizes the human–computer interaction functions such as auxiliary decision making, strategy reasoning, and intelligent search based on the knowledge graph. Using artificial intelligence technology, this article takes the security and stability control strategy of a certain area’s security and stability control system as an example to model and manage. The results show that it can assist the tester to quickly retrieve the strategy, effectively improve the detection efficiency of the security and stability control strategy, avoid the omission and ambiguity caused by the manual understanding of the strategy, and ensure the accuracy and comprehensiveness of the security and stability control strategy detection.

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As the State Grid Multi-cloud IoT platform grows and improves, an increasing number of IoT applications generate massive amounts of data every day. To meet the demands of intelligent management of State Grid equipment, we proposed a scheme for constructing the defect knowledge graph of power equipment based on multi-cloud. The scheme is based on the State Grid Multi-cloud IoT architecture and adheres to the design specifications of the State Grid SG-EA technical architecture. This scheme employs ontology design based on a fusion algorithm and proposes a knowledge graph reasoning method named GRULR based on logic rules to achieve a consistent and shareable model. The model can be deployed on multiple clouds independently, increasing the system's flexibility, robustness, and security. The GRULR method is designed with two independent components, Reasoning Evaluator and Rule Miner, that can be deployed in different clouds to adapt to the State Grid Multi-cloud IoT architecture. By sharing high-quality rules across multiple clouds, this method can avoid vendor locking and perform iterative updates. Finally, the experiment demonstrates that the GRULR method performs well in large-scale knowledge graphs and can complete the reasoning task of the defect knowledge graph efficiently. [ABSTRACT FROM AUTHOR]

This article is devoted to the problem of including a subject area in a semantic library. The subject area is considered, which was not previously presented in part of the ontology of the digital library. A method based on the technology of establishing semantic links with the already accumulated content of the library is proposed. Main idea of the inclusion method is to use dictionaries, thesauri and links to the mathematical encyclopedia. They are used to attach a new scientific publication metadata set to an existing library metadata set. In the course of preliminary processing of texts of scientific publications, their semantic analysis is carried out and a local description of the subject area of this array is formed within the content of the library. Such subject areas as ordinary differential equations, partial differential equations, equations of continuum mechanics, equations of composite mechanics and their solutions expressed in terms of special functions of mathematical physics are considered. The description of the mathematical encyclopedia in its classical form is the central resource. It allows us to identify the appropriate classifiers through the links of the equations of applied problems and their solutions and use them also to form a description of the subject area. This subject area is new and has not been described in our digital library before. Proposed procedure is made possible only by data integration in the library. This takes into account the expansion of the description of subject areas related to the applications of mathematics in interdisciplinary research and technology. The implementation of this procedure is one of the new directions for filling the semantic libraries. It uses the approach of data integration and saturation with connections that are revealed only in the process of analyzing new data. If new references are found in the library during the process of adding a new description, then this method will also make it possible to identify and fill in the gaps in the description of related areas of mathematics, such as applications of solutions to classical problems of mathematical physics. [ABSTRACT FROM AUTHOR]

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, that is, by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualized in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialized for each activity to be recognized. In the article, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualization and reasoning for activity recognition, and to support an iterative development process driven by domain experts. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of filling the gaps in the semantic library based on the distributional semantics of the terms of its thesaurus and the ontology relations. The goal of the study is to fully reflect the actual structure of relations between mathematical subject domains. This is done through identifying context-sensitive semantic relations, and with the use of an algorithm that is based on the word2vec feedforward neural networks. The understanding of the query is analyzed after preliminary processing of set of articles and metadata saturation. The proposed procedure helps to improve the work with the full-text index and, as a result, improves the quality of search in the library. Using a full-text index of a digital semantic library as an example, we demonstrate the process of filling gaps by saturating the semantic relations of the ontology of mathematical subject domains. [ABSTRACT FROM AUTHOR]

Early detection of mild cognitive impairment (MCI) has become a priority in Alzheimer’s disease (AD) research, as it is a transitional phase between normal aging and dementia. However, information on MCI and AD is scattered across different formats and standards generated by different technologies, making it difficult to work with them manually. Ontologies have emerged as a solution to this problem due to their capacity for homogenization and consensus in the representation and reuse of data. In this context, an ontology that integrates the four main domains of neurodegenerative diseases, diagnostic tests, cognitive functions, and brain areas will be of great use in research. Here, we introduce the first approach to this ontology, the Neurocognitive Integrated Ontology (NIO), which integrates the knowledge regarding neuropsychological tests (NT), AD, cognitive functions, and brain areas. This ontology enables interoperability and facilitates access to data by integrating dispersed knowledge across different disciplines, rendering it useful for other research groups. To ensure the stability and reusability of NIO, the ontology was developed following the ontology-building life cycle, integrating and expanding terms from four different reference ontologies. The usefulness of this ontology was validated through use-case scenarios.

The Centre on Persuasive Systems for Wise Adaptive Living (PERSWADE) aims at developing and applying persuasive technologies and system science for social innovation that can help humanity to move toward sustainable, wise, adaptive living. The PERSWADE collaborative knowledge base needs to be designed with the intent to bring together, enrich and logically relate heterogeneous content, such as datasets, scientific literature and any kind of multimedia and social content, to support a participatory approach and help to translate science into action. PERSWADE-CORE, the foundation ontology described in this paper, plays a critical role in this by providing the backbone semantic infrastructure to enable collaboration through efficient data and knowledge integration, sharing and reuse. It also serves the purpose of clarifying and explaining the goals, functions and operations of the Centre. Because of its purpose, PERSWADE-CORE has been designed to be easy-to-use and easy-to-adapt by allowing generic, as well as more specific, relationships among concepts. The PERSWADE approach prioritizes interoperability and relies on the Semantic Web infrastructure. Furthermore, its design is intrinsically aimed at collaborative environments in which ontologies are expected to evolve as a response to users' activity.

Early detection of mild cognitive impairment (MCI) has become a priority in Alzheimer's disease (AD) research, as it is a transitional phase between normal aging and dementia. However, information on MCI and AD is scattered across different formats and standards generated by different technologies, making it difficult to work with them manually. Ontologies have emerged as a solution to this problem due to their capacity for homogenization and consensus in the representation and reuse of data. In this context, an ontology that integrates the four main domains of neurodegenerative diseases, diagnostic tests, cognitive functions, and brain areas will be of great use in research. Here, we introduce the first approach to this ontology, the Neurocognitive Integrated Ontology (NIO), which integrates the knowledge regarding neuropsychological tests (NT), AD, cognitive functions, and brain areas. This ontology enables interoperability and facilitates access to data by integrating dispersed knowledge across different disciplines, rendering it useful for other research groups. To ensure the stability and reusability of NIO, the ontology was developed following the ontology-building life cycle, integrating and expanding terms from four different reference ontologies. The usefulness of this ontology was validated through use-case scenarios. [ABSTRACT FROM AUTHOR]

Cosimulation is an important system-level verification approach aimed at integrating multidomain and multi-physics models during complex system development. Currently, the lack of integrating system development process with cosimulations leads to gaps between them, decreasing the effectiveness and efficiency of system development. Model-based systems engineering (MBSE) tool-chains have been proposed to facilitate the integration of complex system development and automated verification using a model-based approach. However, due to the lack of formal and structured specifications, development information sharing is difficult for supporting MBSE facilitating automated cosimulations. In order to formalize cosimulation in an MBSE tool-chain, a scenario-based ontology is developed in this paper, using formal web ontology language (OWL). Ontology refers to a specification expressing the cosimulation implementations as well as the development information represented in the models supporting the MBSE. It is illustrated by a case study of a cosimulation based on Simulink. Protocol and resource description framework (RDF) query language (SPARQL) and semantic query-enhanced web rule language queries are proposed for evaluating the ontology's completeness and logic for supporting cosimulations. The result demonstrates that the scenario-based ontology formalizes the information related to automated cosimulation development and configurations while using the proposed MBSE tool-chain. [ABSTRACT FROM AUTHOR]