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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]

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]

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]

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]

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]

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]

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]

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]

The article examines issues of web accessibility ecosystems for people with special needs. Methods, models, accessibility standards, and technologies related to the structure, design, and functionality of the web accessibility ecosystem are studied. The stages of developing an accessibility ecosystem are explored. The accessibility of the design, functionalities, structure, and content of a particular ecosystem are presented. Several themes for the design of the system with an emphasis on its accessibility for blind users are explored and analyzed. UX/UI design and the ontological model of accessibility, used in the implementation of the model of the ecosystem and its elements, are studied. A web accessibility ecosystem model has been developed, compliant with the Web Content Accessibility Guidelines and based on semantic technologies. Other qualities of this model are easy access to information resources on the topic of accessibility, convenience for users with different needs, and the possibility of expansion and enrichment in the future. [ABSTRACT FROM AUTHOR]

One of the new approaches to organizing journal articles is using Semantic Web technologies and new "Semantic Publishing" methods based on the ontological models for semantic modeling and representation of these publications. This study aims to design the metadata ontology model for semantic modeling and representation of scholarly journal articles. This research was carried out using the content analysis method. The study population includes articles published in ISC journals (i.e., journals indexed in ISC). A sample of 200 articles from a Persian journal (i.e., "Iranian Journal of Information Processing & Management") and an English journal (i.e., "International Journal of Information Science and Management (IJISM)") were considered for semantic modeling and representation using the metadata ontology model. The data-gathering method was a systematic observation, and the data-gathering tool was a checklist based on the Semantic Publishing and Referencing Ontologies (SPAR Ontologies). This research identified the metadata entities required for semantic modeling and representation of ISC scholarly journal articles. Then, the metadata ontology model was designed using Protege (an ontology editor) and the RDF representation of the model was created in RDF/XML Syntax. After designing the metadata ontology model, the RDF graph of this model was developed to analyze the entities and relationships between them, and its results were analyzed. The results of RDF graph analysis show that the design of the ontology model and the creation of the RDF links between entities significantly impact the semantic description and representation of metadata entities of the ISC scholarly journal articles and the network of relations between them. [ABSTRACT FROM AUTHOR]

Neuronal populations code similar concepts by similar activity patterns across the human brain's semantic networks. However, it is unclear to what extent such meaning-to-symbol mapping reflects distributional statistics, or experiential information grounded in sensorimotor and emotional knowledge. We asked whether integrating distributional and experiential data better distinguished conceptual categories than each method taken separately. We examined the similarity structure of fMRI patterns elicited by visually presented action- and object-related words using representational similarity analysis (RSA). We found that the distributional and experiential/integrative models respectively mapped the high-dimensional semantic space in left inferior frontal, anterior temporal, and in left precentral, posterior inferior/middle temporal cortex. Furthermore, results from model comparisons uncovered category-specific similarity patterns, as both distributional and experiential models matched the similarity patterns for action concepts in left fronto-temporal cortex, whilst the experiential/integrative (but not distributional) models matched the similarity patterns for object concepts in left fusiform and angular gyrus. [ABSTRACT FROM AUTHOR]

In the construction industry, the combination of Industry Foundation Classes (IFC) and semantic web technologies emerges as a promising approach for project information management. However, semantic web technologies are unfamiliar to construction industry practitioners, and there is an absence of useable tools to automate semantic web-based create, read, update, and delete (CRUD) operations on Building Information Modeling (BIM) data. Moreover, the query results from some SPARQL Protocol and RDF Query Language (SPARQL)-based information retrieval methods typically cannot be postprocessed. To overcome these issues, this paper initially proposes a browser/server (B/S) architecture platform prototype to mitigate the operational complexity of the developed system. Subsequently, the core submodules of this platform prototype are developed via Apache Jena and Java Database Connectivity (JDBC) APIs, thereby supporting automatic CRUD operations and result-analyzable SPARQL queries. Furthermore, by leveraging the developed CRUD systems, we propose a feasible method for implementing the soft-coded Shapes Constraint Language (SHACL)-based validation with variable thresholds, and subsequently develop an information compliance checking service for weld size. Eventually, the effectiveness of the developed CRUD system functions and the compliance checking service is validated by conducting case tests. In short, this study proposes a viable approach for the automatic execution of semantic web-based CRUD operations and constraint validation on Resource Description Framework (RDF)-represented information converted from the IFC model. This work contributes to the application of IFC and semantic web technologies within construction project management. [ABSTRACT FROM AUTHOR]

Narratives play a crucial role in human communication, serving as a means to convey experiences, perspectives, and meanings across various domains. They are particularly significant in scientific communities, where narratives are often utilized to explain complex phenomena and share knowledge. This article explores the possibility of integrating large language models (LLMs) into a workflow that, exploiting the Semantic Web technologies, transforms raw textual data gathered by scientific communities into narratives. In particular, we focus on using LLMs to automatically create narrative events, maintaining the reliability of the generated texts. The study provides a conceptual definition of narrative events and evaluates the performance of different smaller LLMs compared to the requirements we identified. A key aspect of the experiment is the emphasis on maintaining the integrity of the original narratives in the LLM outputs, as experts often review texts produced by scientific communities to ensure their accuracy and reliability. We first perform an evaluation on a corpus of five narratives and then on a larger dataset comprising 124 narratives. LLaMA 2 is identified as the most suitable model for generating narrative events that closely align with the input texts, demonstrating its ability to generate high-quality narrative events. Prompt engineering techniques are then employed to enhance the performance of the selected model, leading to further improvements in the quality of the generated texts. [ABSTRACT FROM AUTHOR]

The way by which the news is created and published has been changed by the appearance and the extensive adoption of the Semantic Web (SW) and the concepts of Social Media together with the growth of the Internet. The spread of Fake News (FN) is also prevalent. However, the recognition of FN from the disparate sentences of various website data becomes a challenging task. Hence, an FN detection system is developed in this paper by employing the proposed CKH_GAN model. The process is initiated by gathering data from the scraped dataset, which contains different news and tweets. Then, the acquired data is pre-processed to remove the repeated details, hashtags, and meaningless usernames. Further, the redundant website data are eliminated using the HDFS (Hadoop Distributed File System)-Map Reduce approach. Subsequently, the disparate characteristics, including grammar less and factual less sentences are detected and grouped based on the similarity measures by using the developed FTS-BERT (Fined Tuned Scaling-centered Bidirectional Encoder Representations from the Transformer) algorithm. Further, the informative features are extracted, and the significant features among them are chosen utilizing the proposed CP-EO (Cutting Plane method-centered Equilibrium Optimizer) technique. Eventually, based on the selected features, the FN and real news are categorized using the proposed CKH_GAN (Calculus Krill Herd based Generative Adversarial Networking (CKH_GANs) model for identifying the FN. The experimental outcomes exhibit that the proposed work detected FN with an accuracy of 95.89%, precision of 93.65%, f-score of 94.87%, FPR of 11.23%, and FNR of 10.98%, which are better than the other existing techniques. [ABSTRACT FROM AUTHOR]

The United Nations forecast a significant shift in global population distribution by 2050, with rural populations projected to decline. This decline will particularly challenge mountain areas' cultural heritage, well-being, and economic sustainability. Understanding the economic, environmental, and societal effects of rural population decline is particularly important in Europe, where mountainous regions are vital for supplying goods. The present paper describes a geospatially explicit semantic knowledge graph containing information on 454 European mountain value chains. It is the first large-size, structured collection of information on mountain value chains. Our graph, structured through ontology-based semantic modelling, offers representations of the value chains in the form of narratives. The graph was constructed semi-automatically from unstructured data provided by mountain-area expert scholars. It is accessible through a public repository and explorable through interactive Story Maps and a semantic Web service. Through semantic queries, we demonstrate that the graph allows for exploring territorial complexities and discovering new knowledge on mountain areas' environmental, societal, territory, and economic aspects that could help stem depopulation. [ABSTRACT FROM AUTHOR]

Ontology matching techniques are a solution to surmount the problem of interoperability on the fly between ontologies. However, both alignments and ontologies are likely to be evolved throughout their life cycle, which frequently degrades their qualities. One of the main features of an alignment is its conservativity, so that it should never generate new knowledge compared to those generated by reasoning solely on ontologies. We focus in this paper on the issue of adapting the fresh alignment between evolved OWL-2 ontologies while respecting the conservativity principle. We also propose several patterns to deal with the problem of detection and repair of conservativity breaches during such evolution depending on the type of change in the related OWL-2 ontologies. We use famous ontologies from the field of agriculture to validate our experimentation. At the end we present a set of open research issues. [ABSTRACT FROM AUTHOR]

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology. [ABSTRACT FROM AUTHOR]

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data management. We detail OntoBuilder's architecture, core functionalities, and application scenarios, demonstrating its proficiency and adaptability in addressing complex ontological challenges. Our empirical assessment highlights OntoBuilder's strengths in enabling seamless visualization, automated ontology generation, and robust semantic integration, thereby significantly enhancing user workflows and data management capabilities. The performance of the linked data tools across multiple metrics further underscores the effectiveness of OntoBuilder. [ABSTRACT FROM AUTHOR]

Bridge maintenance is a highly intricate task that involves considering a wide range of factors in order to achieve optimal decisions that align with multiple objectives, criteria, and the entire lifecycle of the bridge. While physics-informed analysis, such as the finite element method (FEM), can simulate complex and closely coupled scenarios, such as bridge structural analysis, it cannot account for some loosely coupled discrete factors, which could be addressed by ontological reasoning. Therefore, this paper presents a knowledge-driven decision-making framework that combines static knowledge reasoning with dynamic FEM analysis results to support holistic bridge maintenance decisions. One significant contribution of this research is the development of a comprehensive bridge maintenance ontology that incorporates knowledge derived from bridge maintenance standards. Another key contribution is the ability to employ complex runtime rules-based reasoning to tackle intricate bridge maintenance scenarios. To enable automatic knowledge-driven reasoning, an integrated workflow is developed to orchestrate semantic modeling with numerical modeling through a Python-based Web Ontology Language application programming interface (OWL API). This integration facilitates the efficient orchestration of the framework. A case study is presented to demonstrate the potential for the developed framework in assisting with the complex holistic decisions required for bridge maintenance. [ABSTRACT FROM AUTHOR]

This article addresses the complex challenges that public organizations face in designing, implementing, and evaluating their strategies, where public interest and regulatory compliance often intertwine with strategic objectives. This research investigates the application of ontologies in the field of public sector strategy management to enhance the capacity of organizations to make informed data-driven decisions, efficiently allocate resources, and effectively navigate the intricate landscape of the public sector. The LNEC - National Laboratory for Civil Engineering's strategy is used as an exploratory case study. Semantic web technologies are used to perform strategy analysis, including validating the strategy formulation and supporting the strategy execution by assessing performance indicators, verifying the design of cause-and-effect relationships between strategic objectives, and monitoring and empirically validating these relationships. The increased interoperability of these technologies enables information sharing across systems and organizations. Following the strategy analysis, recommendations are provided, leading to a more robust and data-driven strategic management approach, enabling accurate, traceable, and continuous monitoring of an organization's strategy. Theoretical and practical implications are discussed, along with limitations and future work. This research offers a blueprint for public sector organizations seeking to optimize their strategies, foster transparency, and deliver more effective services to the public they serve. [ABSTRACT FROM AUTHOR]

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Streaming Linked Data represents a domain within the Semantic Web dedicated to incorporating Stream Reasoning capabilities into the Semantic Web stack to address dynamic data challenges. Such applied endeavours typically necessitate a robust data modelling process. To this end, RDF Stream Processing (RSP) engines frequently utilize OWL 2 ontologies to facilitate this requirement. Despite the rich body of research on Knowledge Representation (KR), even concerning time-sensitive data, a notable gap exists in the literature regarding a comprehensive survey on KR techniques tailored for Streaming Linked Data. This paper critically overviews the key ontologies employed in RSP applications, evaluating their data modelling and KR abilities specifically for Streaming Linked Data contexts. We analyze these ontologies through three distinct KR perspectives: the conceptualization of streams as Web resources, the structural organization of data streams, and the event modelling within the streams. An analytical framework is introduced for each perspective to ensure a thorough and equitable comparison and deepen the understanding of the surveyed ontologies. [ABSTRACT FROM AUTHOR]

The environmental pressure, CO2 emissions (including embodied energy) and delivery risks of our digital infrastructures are increasing. The exponentially growing digitisation of services that drive the transition from industry 4.0 to industry 5.0 has resulted in a rising materials demand for ICT hardware manufacturing. ICT devices such as laptops and data servers are being used on average for 3 and 4–5 years respectively (van Driel (2020)), while research shows that they should last 7 years before replacement (Journal of Cleaner Production69 (2014), 10–16). A solution is to transition from a linear to a circular economy (CE), through which materials that were previously disposed of as waste are re-entered back into product life-cycles through processes such as reuse, recycling, remanufacturing, repurposing. However, the adoption of the CE in the ICT sector is currently limited due to the lack of tools that support knowledge exchange between sustainability, ICT and technology experts in a standardised manner and the limited data availability, accessibility and interoperability needed to build such tools. Further, the already existing knowledge of the domain is fragmented into silos and the lack of a common terminology restricts its interoperability and usability. These also lead to transparency and responsibility issues along the supply chain. For many years now, the Semantic Web has been known to provide solutions to such issues in the form of ontologies. Several ontologies for the ICT, materials and CE domains have been build and successfully utilised to support processes such as predictive maintenance. However, there is a lack of a systematic analysis of the existing ontologies in these domains. Motivated by this, we present a literature survey and analysis of, but not limited to, existing ontologies for ICT devices such as laptops, materials and the CE. In addition, we discuss the need for findable, accessible, interoperable, reusable (FAIR) data in the CE, different factors such as data privacy and security that affect this and the role of ontologies. [ABSTRACT FROM AUTHOR]

Electronic commerce and finance are progressively supporting and including decentralized, shared and public ledgers such as the blockchain. This is reshaping traditional commercial activities by advancing them towards Decentralized Finance (DeFi) and Commerce 3.0, thereby supporting the latter's potential to outpace the hurdles of central authority controllers and lawgivers. The quantity and entropy of the information that must be sought and managed to become active participants in such a relentlessly evolving scenario are increasing at a steady pace. For example, that information comprises asset or service description, general rules of the game, and specific technologies involved for decentralization. Moreover, the relevant information ought to be shared among innumerable and heterogeneous stakeholders, such as producers, buyers, digital identity providers, valuation services, and shipment services, to just name a few. A clear semantic representation of such a complex and multifaceted blockchain-based e-Commerce ecosystem would contribute dramatically to make it more usable, namely more automatically accessible to virtually anyone wanting to play the role of a stakeholder, thereby reducing programmers' effort. However, we feel that reaching that goal still requires substantial effort in the tailoring of Semantic Web technologies, hence this article sets out on such a route and advances a stack of OWL 2 ontologies for the semantic description of decentralized e-commerce. The stack includes a number of relevant features, ranging from the applicable stakeholders through the supply chain of the offerings for an asset, up to the Ethereum blockchain, its tokens and smart contracts. Ontologies are defined by taking a behaviouristic approach to represent the various participants as agents in terms of their actions, inspired by the Theory of Agents and the related mentalistic notions. The stack is validated through appropriate metrics and SPARQL queries implementing suitable competency questions, then demonstrated through the representation of a real world use case, namely, the iExec marketplace. [ABSTRACT FROM AUTHOR]

To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World Wide Web. The knowledge shared on the Web is mostly made available via semi-structured documents written in natural language. To exploit this knowledge, technologies such as Natural Language Processing, Information Extraction, Data mining and the Semantic Web could be used, embedding into documents explicit semantics based on formal knowledge representations such as ontologies. Knowledge representation can be made possible by the deployment of Knowledge Graphs, collections of interlinked representations of entities, events or concepts, based on underlying ontologies. This can assist data analysts to achieve a higher level of situational awareness, facilitating automated reasoning towards different objectives, such as for knowledge management, data maintenance, transparency and cybersecurity. This paper presents a new ontology d2kg [d(iavgeia) 2(to) k(nowledge) g(raph)] integrating in a unique way standard EU ontologies, core and controlled vocabularies to enable exploitation of publicly available data from government decisions and acts published on the Greek platform Diavgeia with the aim to facilitate data sharing, re-usability and interoperability. It demonstrates a characteristic example of a Knowledge Graph based representation of government decisions and acts, highlighting its added value to respond to real practical use cases for the promotion of transparency, accountability and public awareness. The developed d2kg ontology in owl is accessible at: http://w3id.org/d2kg , as well as documented at: http://w3id.org/d2kg/documentation. [ABSTRACT FROM AUTHOR]

LOD4Culture is a web application that exploits Cultural Heritage Linked Open Data for tourism and education purposes. Since target users are not fluid on Semantic Web technologies, the user interface is designed to hide the intricacies of RDF or SPARQL. An interactive map is provided for exploring world-wide Cultural Heritage sites that can be filtered by type and that uses cluster markers to adapt the view to different zoom levels. LOD4Culture also includes a Cultural Heritage entity browser that builds comprehensive visualizations of sites, artists, and artworks. All data exchanges are facilitated through the use of a generator of REST APIs over Linked Open Data that translates API calls into SPARQL queries across multiple sources, including Wikidata and DBpedia. Since March 2022, more than 1.7K users have employed LOD4Culture. The application has been mentioned many times in social media and has been featured in the DBpedia Newsletter, in the list of Wikidata tools for visualizing data, and in the open data applications list of . [ABSTRACT FROM AUTHOR]

The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions. [ABSTRACT FROM AUTHOR]

Emerging distributed intelligence paradigms for the Internet of Things (IoT) call for flexible and dynamic reconfiguration of elementary services, resources and devices. In order to achieve such capability, this paper faces complex interoperability and autonomous decision problems by proposing a thorough framework based on the integration of the Semantic Web of Things (SWoT) and Social Internet of Things (SIoT) paradigms. SWoT enables low-power knowledge representation and autonomous reasoning at the edge of the network through carefully optimized inference services and engines. This layer provides service/resource management and discovery primitives for a decentralized collaborative social protocol in the IoT, based on the Linked Data Notifications(LDN) over Linked Data Platform on Constrained Application Protocol (LDP-CoAP). The creation and evolution of friend and follower relationships between pairs of devices is regulated by means of novel dynamic models assessing trust as a usefulness reputation score. The close SWoT-SIoT integration overcomes the functional limitations of existing proposals, which focus on either social device or semantic resource management only. A smart mobility case study on Plug-in Electric Vehicles (PEVs) illustrates the benefits of the proposal in pervasive collaborative scenarios, while experiments show the computational sustainability of the dynamic relationship management approach. [ABSTRACT FROM AUTHOR]

Despite the public availability of legal documents, there is a need for finding specific information contained in them, such as paragraphs, clauses, items and so on. With such support, users could find more specific information than only finding whole legal documents. Some research efforts have been made in this area, but there is still a lot to be done to have legal information available more easily to be found. Thus, due to the large number of published legal documents and the high degree of connectivity, simple access to the document is not enough. It is necessary to recover the related legal framework for a specific need. In other words, the retrieval of the set of legal documents and their parts related to a specific subject is necessary. Therefore, in this work, we present a proposal of a RDF-based graph to represent and search parts of legal documents, as the output of a set of terms that represents the pursued legal information. Such a proposal is well-grounded on an ontological view, which makes possible to describe the general structure of a legal system and the structure of legal documents, providing this way the grounds for the implementation of the proposed RDF graph in terms of the meaning of their parts and relationships. We posed several queries to retrieve parts of legal documents related to sets of words and the results were significant. [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]

This article focuses on the problematic democratization of small museum collections online in Cyprus. While the web has enabled cultural heritage organizations to democratize information to diverse audiences, numerous small museums do not enjoy the fruits of this digital revolution; many of them cannot democratize their collections online. The current literature provides insight into small and large museums' challenges worldwide. However, we do not have any knowledge concerning small Cypriot museums. This article aims to fulfill this gap by raising the following research question: What is the current state of small museum collections online in Cyprus, and what challenges do they face in democratizing their collections online? We present our empirical results from the interview summaries gathered from six small museums. [ABSTRACT FROM AUTHOR]

Gino Roncaglia's book L'architetto e l'oracolo: forme digitali del sapere da Wikipedia a ChatGPT offers numerous insights into the relationship between ‘cathedrals of knowledge’ and generative artificial intelligence tools. Starting from the theme of fragmentation and complexity, the author delves into the terrain of online encyclopaedias, generative AIs and neural networks, dwelling on the issue of personal memories, and then moves on to the dimension of science fiction and the virtual librarian. Among the topics of interest for those dealing with book and document disciplines in a digital context, the semantic web and its possible developments in the light of the rapidly evolving framework are mentioned.

Project-based organizations (PBOs) rely on project management as their core business process. These organizations are characterized by their disaggregated structure and diverse authority distribution, leading to complications in synchronizing projects, governance, and functional divisions. In this regard, there is a need of a tool to manage this complexity. This paper introduces a novel approach – the IModel- for modeling, designing and analyzing PBOs, following the Design Science Research Method, that seeks to is creating an artifact that solves a real-world problem in a specific context. This artifact is developed through a conceptual integration between project management (PM) and enterprise architecture (EA) sources based on ontologies. Ontologies are powerful, machine-readable graph-structured models which enables sophisticated models’ reasoning and querying. IModel fuses the organizational domain from the ArchiMate EA language with PM insights from PMBOK 7th edition. The resulting IModel has been submitted to an expert’s judgement to assess its intended purposes as well, its potential to be applied in the real-world to support the PBO.

This study explores the use of agile approaches to the creation of ontologies for e-learning, evaluating the benefits and drawbacks as well as the impact on information display. Traditional strategies conflict with the need to fulfill the ever-evolving expectations of users and adapt to the ever-changing features of e-learning environments. The challenge aims to encourage cooperation and versatility in the creation of ontologies for e-learning through the use of Agile standards. Because they make it simpler to organize relationships and statistics, ontologies are vital elements in e-mastering domain names due to the fact that they permit adaptive knowledge of structures and individualized learning experiences. Agile ontology engineering approaches are proposed as a choice for one’s problems, emphasizing flexibility and response. This study highlights the need to work together with customers and incorporate their input into the advent of ontologies. It notably emphasizes using established feedback loops and cooperation with e-learning platform companies. The sensible usefulness and effectiveness of agile methodology for ontology development (AMOD) in e-learning settings are shown through validation efforts in real-global conditions. [ABSTRACT FROM AUTHOR]

Within the contemporary urban development discourse, the paradigm of smart cities has gained prominence over the past two decades. Ensuring sustainability in smart cities requires coherent orchestration of processes that span design, construction, operations, and management. Central to this orchestration are technologies such as Building Information Modeling (BIM), which provides detailed architectural data, and Geographic Information Systems (GIS), which provide comprehensive geographic intelligence. However, a significant challenge remains: data degradation during BIM-GIS integration. This data inconsistency, exacerbated by the different data structures of BIM and GIS, is a barrier to true interoperability. One promising solution to this conundrum is the use of Semantic Web technologies. In this study, we leverage Semantic Linked Data and geometric conversion tools to develop an algorithm that mitigates the loss of semantic information during the BIM-to-GIS conversion process. The effectiveness of this approach is underscored by a 95% accuracy rate of the converted semantic information. [ABSTRACT FROM AUTHOR]

The fast growth of the web generates a significant amount of heterogeneous information such as images, text, audio, and video through various applications. These applications use different layouts to represent significant information. The layouts of table information are overloaded with anomalies that have given rise to intensive research into the semantification of web content and organizing tabular data for knowledge sharing and acquisition. Moreover, there are many anomalies present in tabular layouts that lead to the lack of semantic representation in tabular form and new challenges in data modeling. In this paper, we have discussed the various anomalies present in the tabular data that pertain to ontology learning and population tasks and provide the semantification of tabular data. To complete this task, (1) we provide the list of anomalies that pertain to semantification and provide the resolution to anomalies along with the semantification of tabular data, and (2) we have established the algorithm to interpret the table structure into a formal representation to analyze anomalies and provide the resolution. Furthermore, the proposed approach has been compared with existing approaches using ontology elements, the ability to resolve the anomalies, and the time complexity of the ontology population. [ABSTRACT FROM AUTHOR]

The advent of information technology re-innovates all sectors of bio-sciences. Researchers use Semantic Web to improve web searching, mining and integration, which alleviates the time-consuming task of finding relevant and high-quality content. Semantics is improved through ontology engineering in any domain. Amended and developed ontologies will be uploaded to existing standardised and approved biomedical repositories. The establishment of a World Wide Web Consortium (W3C) approved and standardised ontology repository is the most ambitious goal. This work will solely focus on some selected agri-ontologies. The main objective is to promote outcome-based research and transformation styles of relevant expertise sharing. The intended goal is to win project funding to train and equip students with relevant skills and expertise. Need-based and market-oriented training and professional grooming are a tangible asset for students. The majority of traditional Web development freelancers are unaware of ontology or semantic web market demand. Freelancing is another option for expert Ontology developers. However, agriculture students are used to all the research vocabulary and terminologies in their area, but they do not know how to contribute their expertise to improve the efficiency of the Semantic Web in their domain. If the improvement in relevant ontology becomes a part of the Semantic Web, then it is termed 'Real-time Web semantics enhancement'. In other words, the target ontology becomes a part of the future Web of meaning. [ABSTRACT FROM AUTHOR]

The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, their significance; scenarios of ethical import, and initial ethical guidelines. A focus area, potential case study, and lens through which to see the ethical significance at hand, is ontologies, with emphasis on the most abstract ontologies (so-called generic, foundational, top-level or upper ontologies). This would aim to show the ethical import of highly abstract (often philosophical) ontological choices, assumptions, and their effects on data, developers, users, and society. In particular, I stress the need for critical examination of such models, their content, their intended meanings, assumptions, ideologies, as well as the development process. Both the ethics of, and ethics in, these activities are, therefore, of interest. Topics in the ethics of AI that are relevant include: transparency, bias, data-sharing, privacy, control over data, etc. This paper thereby contributes to the ethics of AI, as well as the ethics of information, of knowledge managment and relevant topics by developing and discussing the ethics of ontology (and similar systems). Future development will refine and add to the ideas expressed in this paper. [ABSTRACT FROM AUTHOR]

In the context of the constant evolution and proliferation of AI technology, hybrid intelligence is gaining popularity in reference to a balanced coexistence between human and artificial intelligence. The term has been extensively used over the past two decades to define models of intelligence involving more than one technology. This paper aims to provide (i) a concise and focused overview of the adoption of ontology in the broad context of hybrid intelligence regardless of its definition and (ii) a critical discussion on the possible role of ontology to reduce the gap between human and artificial intelligence within hybrid-intelligent systems, as well as (iii) the identification of possible future research directions in the field. Alongside the typical benefits provided by the effective use of ontologies at a conceptual level, the conducted analysis has highlighted a significant contribution of ontology to improving quality and accuracy, as well as a more specific role to enable extended interoperability, system engineering and explainable/transparent systems. Additionally, an application-oriented analysis has shown a significant role in present systems (70+% of cases) and, potentially, in future systems. However, despite the relatively consistent number of papers on the topic, a proper holistic discussion on the establishment of the next generation of hybrid-intelligent environments with a balanced co-existence of human and artificial intelligence is fundamentally missed in the literature. Last but not the least, there is currently a relatively low explicit focus on automatic reasoning and inference in hybrid-intelligent systems. [ABSTRACT FROM AUTHOR]

Currently, health tourists primarily rely on independent facilitators to manage and book their medical appointments and vacation plans. There is a notable absence of dedicated booking applications for health tourism packages. This paper proposes HealthTourismHub, an application designed to provide personalized packages that include medical appointments, accommodation options, and recommended tourism activities. It also serves as a platform for medical experts and accommodation providers, allowing health tourists to discover and connect with them, promoting local resources, and contributing to the sustainable growth of health tourism destinations. To incorporate personalization, HealthTourismHub uses an ontology that organizes medical and tourism data, along with a reasoner that generates new knowledge. This approach enables the application to offer customized packages and identify the most suitable providers for each user. Providers are strategically paired and located in close proximity, encouraging shorter travel distances and more efficient travel planning, with the package also including personalized tourism recommendations that benefit the local economy and contribute to a conscious tourism industry. A survey was conducted to assess the usability of the application and general perspectives towards health tourism, including motivations, concerns, and preferences. The results revealed an above-average SUS score, indicating that users found the application user-friendly and effective. Some areas for improvement were identified, such as error handling and additional functionalities. Nonetheless, HealthTourismHub shows great potential as a pioneer in the field of sustainable health tourism applications. [ABSTRACT FROM AUTHOR]

This article examines the evolution of artists' interaction with the audience in the context of media culture, linked to the updates of communication mechanisms on the Internet, from the static Web 1.0 to the interactive Web 3.0. The aim of the article is to show the connections between the changes in authorship models and mechanisms of interaction with the audience in artistic practice and the evolutionary development of web technologies. The transformation of contemporary artistic forms is the result of the active influence of digital technologies and the associated collapse of the existing hierarchy between author, performer and audience. The study discusses the strengthening of the interactive component in artistic practices, parallel to the development of Internet technologies from Web 1.0 to the semantic Web 3.0 and analyzes the overlapping of these processes and their mutual influence in the context of post humanist perspectives. It was found that artistic collaborations through collective authorship are a characteristic model of authorship for this phase of Internet development. It was noted that the evolution from Web 2.0 to Web 3.0 opens new possibilities for the creation of multisensory experiences, thanks to the use of VR (Virtual Reality) and AR (Augmented Reality) technologies, which are actively used by artists in artistic practices and works, for example in operas or concerts. In addition, the active development and integration of artificial intelligence technologies in artistic practices has been identified. By analyzing musical projects of different genres and forms, the transformative influence of web culture on contemporary artistic practices becomes clear. [ABSTRACT FROM AUTHOR]

This paper researched on the query method of web education resources based on semantic topic similarity. This paper proposes a hierarchical model of web Education. This model is a two-tier filtering model based on mobile agent. The model first classifies and filters according to semantic similarity, and then filters web education resources according to Q-learning filtering algorithm in machine learning. This paper presents a kind of rank algorithm for web educational resources. According to Euclid ambiguity and RSS aggregation technology, this algorithm finds the web education resources that users need. First of all, RSS document aggregation technology is used to quickly gather the music education resources that users need. Secondly, the query is characterized by Euclid fuzziness in fuzzy sets. The fuzziness of the association between content and resources. Finally, we get the personalized resources to satisfy the end users of web education resources. [ABSTRACT FROM AUTHOR]

This ongoing study aims to map the technological and systemic challenges found in the process of implementing connected open data practices in libraries to make their catalogs available online. The study is developed as applied and exploratory research, based on the use of bibliographic and documentary research. The technique adopted is Bardin's Content Analysis, comprising the phases of elaboration of records for the initial evaluation of the texts, creation of categories for analysis, grouping in the respective created categories, followed by data analysis and interpretation. It is expected to achieve results that identify the challenges and potential of bibliographic records for use in open data format and the benefits provided by connecting these data. [ABSTRACT FROM AUTHOR]

To enable machines to process state-of-practice Web API documentation, we propose a Transformer model for the generic task of identifying a Web API element within a syntax structure that matches a natural language query. We solve this semantic-search task with Transformer-based question answering and demonstrate the applicability of our approach to two different tasks, namely the discovery of endpoints and the identification of parameters in payload schemas. With samples from 2321 OpenAPI documentation, we prepare different datasets and fine-tune pre-trained BERT models to these two tasks. We evaluate the generalizability and the robustness of our fine-tuned models. We achieve accuracies of 81.95% for the parameter-matching and 88.44% for the endpoint-discovery task. [ABSTRACT FROM AUTHOR]

Presented as a research challenge in 2001, the Semantic Web (SW) is now a mature technology, used in several cross‐domain applications. One of its key benefits is a formal semantics of its RDF data format, which enables a system to validate data, infer implicit knowledge by automated reasoning, and explain it to a user; yet the analysis presented here of 71 RDF‐based SW systems (out of which 17 reasoners) reveals that the exploitation of such semantics varies a lot among all SW applications. Since the simple enumeration of systems, each one with its characteristics, might result in a clueless listing, we borrow from Software Engineering the idea of maturity model, and organize our classification around it. Our model has three orthogonal dimensions: treatment of blank nodes, degree of deductive capabilities, and explanation of results. For each dimension, we define 3–4 levels of increasing exploitation of semantics, corresponding to an increasingly sophisticated output in that dimension. Each system is then classified in each dimension, based on its documentation and published articles. The distribution of systems along each dimension is depicted in the graphical abstract. We deliberately exclude resources consumption (time and space) since it is a dimension not peculiar to SW. This article is categorized under:Fundamental Concepts of Data and Knowledge > Knowledge RepresentationFundamental Concepts of Data and Knowledge > Explainable AI [ABSTRACT FROM AUTHOR]

Project-based organizations (PBOs) rely on project management as their core business process. These organizations are characterized by their disaggregated structure and diverse authority distribution, leading to complications in synchronizing projects, governance, and functional divisions. In this regard, there is a need of a tool to manage this complexity. This paper introduces a novel approach – the IModel - for modeling, designing and analyzing PBOs, following the Design Science Research Method, that seeks to is creating an artifact that solves a real-world problem in a specific context. This artifact is developed through a conceptual integration between project management (PM) and enterprise architecture (EA) sources based on ontologies. Ontologies are powerful, machine-readable graph-structured models which enables sophisticated models' reasoning and querying. IModel fuses the organizational domain from the ArchiMate EA language with PM insights from PMBOK 7th edition. The resulting IModel has been submitted to an expert's judgement to assess its intended purposes as well, its potential to be applied in the real-world to support the PBO. [ABSTRACT FROM AUTHOR]