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The composition of web service is the best chance provided by Service-Oriented Computing and Service-Oriented Architecture as it gives real competitive benefits for some industrial and technological actors via presenting them with the probability to guarantee fast and inexpensive improvement of collaborative and distributed software applications. Here, a novel technique is introduced, which contains several phases for better web services. At first, the requirements specification phase is enabled with a set of requirements such as non-functional and functional requirements. Next to the requirements specification stage, the discovery stage is enabled to choose the suitable web services that have high-matching profiles with the developer's requirement set. Here, for a semantic matching algorithm, a new hybrid similarity measure is developed. Additionally, among the group of candidate services that the discovery phase returned, the best service is selected during the selection step. Then, hybrid Squeeze_Long Short-Term Memory (Squeeze_LSTM) is used for choosing the best service and it is designed by the formation of SqueezeNet and LSTM. The Semantic Web Services are finally implemented. The efficiency of the Squeeze_LSTM is evaluated and has achieved a superior precision of 0.909, recall of 0.890, and response time of 6.461S. [ABSTRACT FROM AUTHOR]

Internet of Things aims to simplify and automate complicated tasks by using sensors and other inputs for collecting huge amounts of data, processing them in the cloud and on the edge networks, and allowing decision making toward further interactions via actuators and other outputs. As connected IoT devices rank in billions, semantic interoperability remains one of the permanent challenges, where ontologies can provide a great contribution. The main goal of this paper is to analyze the state of research on semantic interoperability in well-being, aging, and health IoT services by using ontologies. This was achieved by analyzing the following research questions: "Which IoT ontologies have been used to implement well-being, aging and health services?" and "What is the dominant approach to achieve semantic interoperability of IoT solutions for well-being, aging and health?' We conducted a scoping literature review of research papers from 2013 to 2024 by applying the PRISMA-ScR meta-analysis methodology with a custom-built software tool for an exhaustive search through the following digital libraries: IEEE Xplore, PubMed, MDPI, Elsevier ScienceDirect, and Springer Nature Link. By thoroughly analyzing 30 studies from an initial pool of more than 80,000 studies, we conclude that IoT ontologies for well-being, aging, and health services increasingly adopt Semantic Web of Things standards to achieve semantic interoperability by integrating heterogeneous data through unified semantic models. Emerging approaches, like semantic communication, Large Language Models Edge Intelligence, and sustainability-driven IoT analytics, can further enhance service efficiency and promote a holistic "One Well-Being, Aging, and Health" framework. [ABSTRACT FROM AUTHOR]

ABSTRACT Globalization and growing business dynamics lead to weakly harmonized supply chain (SC) systems. While smart technology offers innovation opportunities, supply chains often lack the integration needed to fully leverage resources and collaboration. A comprehensive systems engineering (SE)‐driven model for integrated innovation and optimization of smart SC business models is still missing. This study, through case research at SAP SE's Industry 4.0 division and three automotive companies, identifies key digital transformation objectives and interoperability gaps hindering smart opportunities. Systems engineering, supply chain management (SCM), and artificial intelligence (AI) methods were synthesized into a holistic SE‐driven model for transforming and optimizing SC business models. This model integrates management concepts like the theory of ambidexterity and dynamic capabilities, with SE methods capability engineering and complex adaptive systems, and semantic web concepts. Key SE contributions include meta‐modeling multi‐tier SC architectures, ensuring performance and resilience via simulations, and balancing value exploration and exploitation. Moreover, semantic harmonized and profit‐optimized SC ecosystems enable collaborative innovation for flexible, efficient manufacturing—a core Industry 4.0 principle. This SE‐driven model, validated by experts, provides a concise view of digital SC business models and a driver of generative design. [ABSTRACT FROM AUTHOR]

The FunDivEurope (Functional Significance of Forest Biodiversity in Europe) project aimed to quantify the role of forest biodiversity for ecosystem functioning and the delivery of goods and services in major European forest types. Members of the research community aimed to communicate the research findings related to the functional significance of forest biodiversity to the wider public. Therefore, a web-based Knowledge Transfer Platform (KTP) was designed to ensure project-generated knowledge is transferred to targeted stakeholders and user groups. The paper shows a user experience-based approach in the development of the knowledge transfer platform, and provides insights into the system architecture to show how semantic web-based technologies are able to target a broader audience while keeping entry barriers as low as possible to support communities of practice to grow. [ABSTRACT FROM AUTHOR]

Background Precision therapies, such as targeted and immunotherapies, have substantially changed the landscape of late-stage non–small cell lung cancer (NSCLC). Yet, utilization of these therapies is disproportionate across strata defined by race and socioeconomic status, possibly because of disparities in molecular diagnostic testing (or biomarker testing), which is a prerequisite to treatment. Methods We extracted a cohort of NSCLC patients from the Surveillance, Epidemiology, and End Results–Medicare linked data. The primary outcome was receipt of a molecular diagnostic test, based on claims data. The primary predictors were race and socioeconomic status. Likelihood of receiving a molecular diagnostic test and overall survival were investigated using logistic and Cox proportional hazards regression, adjusted for sex, age, residence, histology, marital status, and comorbidity. Results Of the 28 511 NSCLC patients, 11 209 (39.3%) received molecular diagnostic testing. Compared with White patients, fewer Black patients received a molecular diagnostic test (40.4% vs 27.9%; P  < .001). After adjustment, Black patients (adjusted odds ratio [OR] = 0.64, 95% confidence interval [CI] = 0.58 to 0.71) and those living in areas with greater poverty (adjusted OR = 0.85, 95% CI = 0.80 to 0.89) had statistically significant decreased likelihood of molecular diagnostic testing. Patients who did receive testing had a statistically significant decreased risk of death (adjusted hazard ratio [HR] = 0.74, 95% CI = 0.72 to 0.76). These results held in the stratified analysis of stage IV NSCLC patients. Conclusion Disparities exist in comprehensive molecular diagnostics, which is critical for clinical decision making. Addressing barriers to molecular testing could help close gaps in cancer care and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Machine learning revolutionizes accuracy in diverse fields such as disease diagnosis, speech understanding, and sentiment analysis. However, its intricate architecture often obscures the decision-making process, creating a "black box" that hinders trust and limits its potential. This lack of transparency poses significant challenges, particularly in critical fields like the healthcare system. We present OntoXAI, a Semantic Web Rule Language (SWRL) based Explainable Artificial Intelligence (XAI) approach to address these challenges. OntoXAI leverages semantic technology and machine learning (ML) to enhance prediction accuracy and generate user-comprehensible natural language explanations in the context of dengue disease classification. OntoXAI can be summarized into three key aspects. (1) Creates a knowledge base that incorporates domain-specific knowledge related to the disease. This allows for the integration of expert knowledge into the classification process. (2) OntoXAI presents a diagnostic classification system that utilizes patient symptoms as input to classify the disease accurately. By leveraging ML algorithms, it achieves high accuracy in disease classification. (3) OntoXAI introduces SWRL and ontology to integrate explainable AI techniques with Open AI API, enabling a better understanding of the classification process. By combining the power of machine learning algorithms with the ability to provide transparent, human-understandable explanations through Open AI API, this approach offers several advantages in enhancing prediction accuracy, achieving levels of up to 96%. Overall, OntoXAI represents a significant advancement in the field of explainable AI, addressing the challenges of transparency and trust in machine learning systems, particularly in critical domains like healthcare. [ABSTRACT FROM AUTHOR]

Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. Using elephant GPS data extracted from an ontology-based knowledge graph, PoachNet employs a sequential neural network to predict future movements, which are semantically modelled and incorporated into the graph. Semantic Web Rule Language (SWRL) is applied to infer poaching risk based on these geo-location predictions and poaching rule-based logic. By addressing spatiotemporal complexity and integrating predictions into an actionable semantic rule, PoachNet advances the field, with its geo-location prediction model outperforming state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Compliance inspections and building analysis are critical to the success of any construction project. At present, such assessments are primarily conducted manually by experts in the architecture, engineering and construction (AEC) sector resulting in a tedious, labor‐intensive, and generally inefficient undertaking. Yet, with the gradual adoption of Building Information Modeling (BIM), automated building analysis and compliance checking become feasible. The Industry Foundation Classes (IFC) has received a lot of traction in the AEC industry as a vendor‐neutral data model. Its well‐defined semantics can be exploited by reasoning engines that allow for semantic reasoning on building models, the core mechanism required for automated compliance checking and building analysis. In this paper, a general‐purpose model checking framework for IFC building models, as well as an appropriate specification layout are introduced. Model checking via semantic reasoning is realized using various technologies from the Semantic Web. To present and evaluate our implementation, a sample specification is developed and tested on two IFC building models. [ABSTRACT FROM AUTHOR]

Archives that house digital collections often struggle with rapidly evolving workflows and the intrinsic difficulties in managing disordered records. Both physical and digital records may have complex relationships with other records such as drafts of the same document or one document that is included in another, but digital records offer the possibility that a computer may analyze the collection and automatically discover such relationships. An analytical tool for digital collections would employ a model that can represent the network of relationships between files instead of the hierarchical model used in traditional archival arrangement and description. A proof-of-concept of such a tool, employing techniques such as fuzzy and perceptual hashes, demonstrates the viability of this approach and suggests avenues for future research and development. [ABSTRACT FROM AUTHOR]

Automated Compliance Checking (ACC) has emerged as a critical tool for enforcing legal regulations across various domains. This paper contributes to ongoing research in Semantic Web technologies, particularly focusing on the execution of SHACL-SPARQL rules on RDF data. The RDF, being one of the most widely used knowledge representation (KR) formats, serves as the foundation of our approach, ensuring compatibility with existing standards and enhancing interoperability. Our research enhances the aggregate and temporal aspects of ACC by addressing the limitations of traditional ACC methodologies, which often fall short in managing the nuanced temporal and aggregate requirements essential for legal reasoning. Through a case study analysis of selected regulations with aggregate and temporal facets in LI 2204, which regulates local content and participation in Ghana's upstream petroleum industry, this paper demonstrates the effectiveness of the proposed solution in achieving these dimensions of ACC. The findings underscore the potential of Semantic Web technologies to transform ACC practices by moving towards standardized, interoperable solutions. All source codes are freely available online together with instructions to locally reproduce the simulations. [ABSTRACT FROM AUTHOR]

Previous research examining how icons' concreteness, visual complexity, and distinctiveness influence visual search performance have led to disagreements over which icon characteristic most affects behaviour. These icon characteristics are often poorly defined and interrelated, particularly concreteness. Accordingly, drawing strong inferences about the robustness of concreteness as a factor in search for visual icons is challenging. Here, we operationalised concreteness into three distinct levels: concrete icons were images of real-world objects, photorealistic icons were drawings of the object, and abstract icons were images with no conceptual information. Across two experiments, participants rated each icon on various icon characteristics (e.g. concreteness, visual complexity) to provide a ground truth for these factors and to validate our concreteness manipulation. In a separate study, naive participants performed a visual search task for a target icon. Oculomotor measures were utilised to elucidate how various icon characteristics affected search performance. Although we were unable to fully disassociate concreteness from visual complexity, we found that icons high in concreteness improved search performance, but as visual complexity increased, object identification became slower. This was largely demonstrated through increased verification times for complex targets. The present set of studies indicate that highly concrete and simple icons engender search benefits. [ABSTRACT FROM AUTHOR]

Despite being a relatively recent innovation, the World Wide Web has undergone rapid expansion, profoundly influencing society on a global scale. At its core, the Web is a vast network of interconnected documents primarily designed for human consumption, making it challenging for machines to interpret and process this information autonomously. The Semantic Web emerges as a transformative extension of the current Web, aiming to bridge this gap. By enabling information to be structured in a way that machines can interpret, the Semantic Web facilitates seamless machine-to-machine communication and interaction, opening new possibilities for automation, efficiency, and intelligence in handling digital information. This paper explores the intrinsic relationship between the Semantic Web and Web services, highlighting how these technologies work in tandem to enhance machine interpretability. It delves into the pivotal role of supporting technologies, such as RDF, SPARQL, and ontologies, in enabling this interaction. Through this analysis, the paper underscores the potential of these innovations to revolutionize information exchange and interoperability on the Web. [ABSTRACT FROM AUTHOR]

The growth of Semantic Web and Web3 technology wishes sophisticated frameworks for interoperability, records representation and querying. The studies introduces the key Semantic Web technologies, which evolve Resource Description Framework, RDF Schema(RDF), Web Ontology Language(OWL), SPARQL Protocol and RDF Query Language(SPARQL) and the Linked Data ideas, exploring their programs and ontology engineering. By exploring those technology, the authors intention to beautify information integration and semantic interoperability across systems, building extra shrewd and connected internet environments. [ABSTRACT FROM AUTHOR]

Multilingual Knowledge Graphs (MKGs) have emerged as a crucial component in various natural language processing tasks, enabling efficient representation and utilization of structured knowledge across multiple languages. One can get data, information, and knowledge from various sectors, like libraries, archives, institutional repositories, etc. Variable quality of metadata, multilingualism, and semantic diversity make it a challenge to create a digital library and multilingual search facility. To accept these challenges, there is a need to design a framework to integrate various structured and unstructured data sources for integration, unification, and sharing databases. These are controlled using linked data and semantic web approaches. In future, multilingual knowledge graph overcomes all the linguistic nuances, technical barriers like semantic interoperability, data harmonization etc and enhance cooperation and collaboration throughout the world. Through a comprehensive analysis of the current state-of-the-art techniques and ongoing research efforts, this paper aims to offer insights into the future directions and potential advancements in the field of Multilingual Knowledge Graphs. This paper deals with a multilingual knowledge graph and how to build up a multilingual knowledge graph. It also focuses on the various challenges and opportunities for designing multilingual knowledge graphs. [ABSTRACT FROM AUTHOR]

As an important data source, the Building Information Model (BIM) plays an important role in modern building safety management. Numerous studies have closely examined automatic compliance inspections for building safety and the safety management of dangerous projects. However, the value of the BIM has not been fully exploited in evidence-based practices of building safety. To address this limitation, this paper proposes an ontology-based Evidence/Risk-Based Safety (ERBS) management framework for divisional and subdivisional works with high risk, which includes: (1) BIM data extraction based on dynamo; (2) creation of an ontology based on building information and the ERBS management process model; (3) converting BIM data and evidence into ontology individuals; and (4) integrating the ontology through semantic web technology and using the Semantic Web Rule Language (SWRL) to conduct rule-based reasoning on the ontology. A case study shows that the framework is effective for the ERBS management of divisional and subdivisional works with high risk. The framework proposed in this study provides effective safety management methods for high-risk projects that can be applied in wider engineering practice in the future. [ABSTRACT FROM AUTHOR]

One of the issues in Natural Language Processing (NLP) and Artificial Intelligence (AI) is language representation and modeling, aiming to manage its structure and find solutions to linguistic issues. With the pursuit of the most efficient capture of knowledge about the Modern Greek language and, given the scientifically certified usability of the ontological structuring of data in the field of the semantic web and cognitive computing, a new ontology of the Modern Greek language at the level of structure and vocabulary is presented in this paper, using the Protégé platform. With the specific logical and structured form of knowledge representation to express, this research processes and exploits in an easy and useful way the distributed semantics of linguistic information. [ABSTRACT FROM AUTHOR]

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.

In this era of semantic web, libraries can leverage the power of Wikdata to enhance the discoverability of their resources, foster interoperability, as well as empower information seekers to navigate a richer and vast network of knowledge bases. This study adopted the review method to theoretically examine the concept of Wikidata and how libraries could benefit from the technology by linking their metadata to the global knowledgebase, so as to increase the visibility and discoverability of library materials. The study X-rayed the application of Wikidata in libraries, particularly in the area of increasing visibility and discoverability of library of resources. With Wikidata, libraries could create a web of interconnected knowledge bases that transcends boundaries. It emphasized the importance of integrating Wikidata into libraries, as this could help to shape the open knowledge ecosystem and empower libraries to better serve their user communities in an ever-changing World. The study further advocates for the adoption of Wikidata in libraries so as to project library resources for the greater good of mankind.

Power infrastructure projects are characterized by complex supply chain structures and numerous stakeholders, presenting significant challenges in maintaining data integrity and ensuring seamless integration of project information. Previous Digital Twins (DTs) and Building Information Modeling (BIM) collaboration methods lack robust mechanisms for data traceability and immutable storage, leading to potential risks such as data loss or tampering. Furthermore, existing project information exchange and data management methods do not adequately integrate diverse data types, such as project documentation, onsite environment monitoring IoT sensor readings and CAD/BIM-based design information. This research introduces a novel DT data platform prototype, utilizing Blockchain and Semantic Web technologies, to establish a trustworthy DT data environment for power infrastructure projects. This system collects heterogeneous data, including manual inputs and IoT-generated data, and processes them into RDF format on dedicated devices. The integrated data is then stored on a Permissioned Blockchain, ensuring traceability and immutability. The framework incorporates Distributed File Systems to enhance storage efficiency and features a semantic gateway that transforms heterogeneous data into RDF graphs, fostering interoperability and the potential for automated data linkage. The efficacy of this prototype was demonstrated through a case study, testing data consistency and showcasing prototype queries enhanced by Semantic Web, thus substantiating the platform's capacity to support multidisciplinary project management. [ABSTRACT FROM AUTHOR]

IoT devices produce a vast amount of data ranging from personal to sensitive information. Usually, these devices remain connected to the internet so protecting the information produced by them is crucial. Since most of the IoT devices are resource-constrained, they must be supported with lightweight encryption standards to protect information. Recent research has used the concept of context awareness to select the most suitable data encryption standard based on the device resources along with the required information confidentiality level. However, to effectively use the context information, it is required to be organized explicitly while considering the dynamic nature of IoT systems. In this regard, ontology-based systems effectively reduce the volume of manual work while recommending solutions. Currently, these systems cannot work with precision due to multiple uncertain factors of IoT sensory data. To overcome this challenge, this research proposes a fuzzy ontology-based context-aware system to protect IoT device information with the help of an encryption algorithm that considers device capabilities and user priorities regarding the data confidentiality. In order to automate the recommendation process, Semantic Web Rule Language (SWRL) rules and fuzzy logic are used, whereas, Description Logic and RDF Query Language is used to evaluate the results. The evaluation results confirm that the proposed method can produce results according to human perception by significantly increasing the accuracy of prediction. [ABSTRACT FROM AUTHOR]

Information on restoration science and practice is dispersed across large numbers of scientific papers, reports, books, and other resources, and there is a lack of synthetic approaches and of linkages between ecological theory and practice. With recent calls for scaling up ecological restoration, there is an urgent need for improving the effectiveness of restoration ecology by presenting existing knowledge in an organized and accessible form. Practitioners benefit from knowing which theories explain patterns and processes in a specific ecosystem, and scientists need an overview of empirical evidence supporting current theories. Strengthening links between restoration practice and science benefits both areas. Based on a new approach used for organizing and assessing hypotheses in invasion biology, we suggest the development of an interactive online platform that promotes the integration of restoration science and practice by (1) presenting an overview of restoration ecology; (2) mapping theoretical work relevant for ecological restoration; (3) displaying direct links to relevant publications; and (4) providing summaries of empirical evidence for ecological theories in specific settings. This online knowledge base should be developed in an open process, bringing together the restoration community with experts in semantic web and natural language processing, library scientists, web designers, and other specialists. The platform should become an evolving, searchable, openly accessible, and intuitively organized tool for future ecological restoration. [ABSTRACT FROM AUTHOR]

The Solid project aims to empower people with control over their own data through the separation of data, identity, and applications. The goal is an environment with clear interoperability between all servers and clients that adhere to the specification. Solid is a standards-driven way to extend the Linked Data vision from public to private data, and everything in between. Multiple implementations of the Solid Protocol exist, but due to the evolving nature of the ecosystem, there is a strong need for an implementation that enables qualitative and quantitative research into new features and allows developers to quickly set up varying development environments. To meet these demands, we created the Community Solid Server, a modular server that can be configured to suit the needs of researchers and developers. In this article, we provide an overview of the server architecture and how it is positioned within the Solid ecosystem. The server supports many orthogonal feature combinations on axes such as authorization, authentication, and data storage. The Community Solid Server comes with several predefined configurations that allow researchers and developers to quickly set up servers with different content and backends, and can easily be modified to change many of its features. The server will help evolve the specification, and support further research into Solid and its possibilities. [ABSTRACT FROM AUTHOR]

BACKGROUND: Chronic pain is the type of pain that healthcare professionals frequently encounter. Health care students' attitudes towards pain management are not sufficient and this negatively affects their chronic pain management. When students cannot manage the chronic pain they will experience professional burnout, depersonalization, and a decrease in compassion and empathy in patient care. Therefore, the first step in improving health care students' attitudes towards patients with chronic pain is to determine their attitudes. OBJECTIVE: This study aims to test the validity and reliability of the Scale for Healthcare Professionals' Attitudes towards Patients with Chronic Pain (HCPAPCP Scale) in healthcare students. METHOD: This quantitative study was conducted with 205 health care students in January-February 2022. Data were collected online with Personal Information Form and the HCPAPCP Scale. To determine the reliability of the scale, internal consistency and test-retest, and for construct validity, exploratory factor analysis and confirmatory factor analysis were performed. RESULTS: The results of the exploratory factor analysis showed that the two-factor scale consisting of 18 items, the factor structure, and the distribution of factors in items were the same as the findings of the original scale. The Cronbach's Alpha coefficient was 0.88 for the first factor and 0.74 for the second factor. Test-retest reliability was 0.60. In confirmatory factor analysis, the model had a good and acceptable fit. CONCLUSION: We found that the HCPAPCP Scale was valid and reliable in healthcare students. [ABSTRACT FROM AUTHOR]

Spatial data play a critical role in various domains such as cadastre, environment, navigation, and transportation. Therefore, ensuring the quality of geospatial data is essential to obtain reliable results and make accurate decisions. Typically, data are generated by institutions according to specifications including application schemas and can be shared through the National Spatial Data Infrastructure. The compliance of the produced data to the specifications must be assessed by institutions. Quality assessment is typically performed manually by domain experts or with proprietary software. The lack of a standards-based method for institutions to evaluate data quality leads to software dependency and hinders interoperability. The diversity in application domains makes an interoperable, reusable, extensible, and web-based quality assessment method necessary for institutions. Current solutions do not offer such a method to institutions. This results in high costs, including labor, time, and software expenses. This paper presents a novel framework that employs an ontology-based approach to overcome these drawbacks. The framework is primarily based on two types of ontologies and comprises several components. The ontology development component is responsible for formalizing rules for specifications by using a GUI. The ontology mapping component incorporates a Specification Ontology containing domain-specific concepts and a Spatial Data Quality Ontology with generic quality concepts including rules equipped with Semantic Web Rule Language. These rules are not included in the existing data quality ontologies. This integration completes the framework, allowing the quality assessment component to effectively identify inconsistent data. Domain experts can create Specification Ontologies through the GUI, and the framework assesses spatial data against the Spatial Data Quality Ontology, generating quality reports and classifying errors. The framework was tested on a 1/1000-scale base map of a province and effectively identified inconsistencies. [ABSTRACT FROM AUTHOR]

As a critical business activity in the aircraft life cycle, maintenance processes are highly complex and require multidisciplinary knowledge. Knowledge integration and representation oriented toward aircraft maintenance processes are necessary to improve work efficiency. Nonetheless, conventional approaches lack effective unified management, which obstructs domain knowledge sharing and ultimately impedes maintenance work. In this context, this paper proposes a knowledge representation framework based on the benefits of ontology, which formalizes multidisciplinary knowledge for aircraft maintenance processes. An ontology of aircraft maintenance processes is developed for knowledge conceptualization and reuse. On this basis, a domain knowledge extraction model based on the bidirectional encoder representation from transformers (BERT) is constructed to automatically extract entities and relationships related to maintenance processes. With a series of Semantic Web Rule Language (SWRL) rules, a knowledge reasoning method is proposed based on the aircraft maintenance process ontology to mine hidden knowledge. We evaluate the developed ontology and demonstrate the feasibility and usefulness of the proposed knowledge reasoning method in a case study. The results show that the proposed knowledge representation framework provides an effective knowledge formalization method for complex knowledge in aircraft maintenance processes to support work optimization. [ABSTRACT FROM AUTHOR]

Introduction: Modern forestry increasingly relies on the management of large datasets, such as forest inventories and land cover maps. Governments are typically in charge of publishing these datasets, but they typically employ disparate data formats (sometimes proprietary ones) and published datasets are commonly disconnected fromother sources, including previous versions of such datasets. As a result, the usage of forestry data is very challenging, especially if we need to combine multiple datasets. Methods and results: Semantic Web technologies, standardized by the World Wide Web Consortium (W3C), have emerged in the last decades as a solution to publish heterogeneous data in an interoperable way. They enable the publication of self-describing data that can easily interlink with other sources. The concepts and relationships between them are described using ontologies, and the data can be published as Linked Data on the Web, which can be downloaded or queried online. National and international agencies promote the publication of governmental data as Linked Open Data, and research fields such as biosciences or cultural heritage make an extensive use of SemanticWeb technologies. In this study, we present the result of the European Cross-Forest project, addressing the integration and publication of national forest inventories and land cover maps from Spain and Portugal using Semantic Web technologies. We used a bottom-up methodology to design the ontologies, with the goal of being generalizable to other countries and forestry datasets. First, we created an ontology for each dataset to describe the concepts (plots, trees, positions, measures, and so on) and relationships between the data in detail. We converted the source data into Linked Open Data by using the ontology to annotate the data such as species taxonomies. As a result, all the datasets are integrated into one place this is the Cross-Forest dataset and are available for querying and analysis through a SPARQL endpoint. These data have been used in real-world use cases such as (1) providing a graphical representation of all the data, (2) combining it with spatial planning data to reveal the forestry resources under themanagement of Spanish municipalities, and (3) facilitating data selection and ingestion to predict the evolution of forest inventories and simulate how different actions and conditions impact this evolution. Discussion: The work started in the Cross-Forest project continues in current lines of research, including the addition of the temporal dimension to the data, aligning the ontologies and data with additional well-known vocabularies and datasets, and incorporating additional forestry resources. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system’s ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the F2 coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (F2TE2 = 0.830, only below bagging, F2BAG = 0.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system’s versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI. [ABSTRACT FROM AUTHOR]