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The mapping method that is based on the name and structure of the ontology elements is the strategy used in most mapping methods. Methods using the name often only use the similarity between the individual elements in the ontology to predict the semantic relations between two ontologies, while the latter measure the mapping between two ontologies by means of the structural relations between the elements. The effects of these two kinds of mapping strategies are not ideal. Addressing this issue, the work presented in this paper proposes an ontology mapping approach, in which the ontology element name and structure are combined. It uses the approaches based on linguistics and distance to generate a variable weight semantic graph. On this graph, the similarity of element names and structure are calculated through iterative computation. In the process of iteration, similarity result values are constantly adjusted. The approach avoids the problem of single methods that cannot use the entire amount of ontology information; therefore, it provides a more ideal mapping result. For making full use of the message of ontology, our implementation and experimental results are provided to demonstrate the effectiveness of the mapping approach. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for assessing text coherence. Central to this approach is an ontology‐based representation of text, which captures the level of relatedness between consecutive sentences via ontologies. Our method encompasses annotating text using ontological concepts and assessing text coherence based on relatedness measurement among these concepts. The ontology‐based relatedness measurement method used in this study considers various types of relationships in ontologies and derived relationships via an inference engine for computing relatedness. We hypothesized that rich variety of relationships and inferred facts in ontologies would improve the success of text coherence assessment. Our results demonstrate that the use of ontologies yields to coherence values that have a higher correlation with human ratings. [ABSTRACT FROM AUTHOR]

The paper proposes an alternative approach to improve the performance of image retrieval. In this work, a framework for image retrieval based on machine learning and semantic retrieval is proposed. In the preprocessing phase, the image is segmented objects by using Graph‐cut, and the feature vectors of objects presented in the image and their visual relationships are extracted using R‐CNN. The feature vectors, visual relationships, and their symbolic labels are stored in KD‐Tree data structures which can be used to predict the label of objects and visual relationships later. To facilitate semantic query, the images use the RDF data model and create an ontology for the symbolic labels annotated. For each query image, after extracting their feature vectors, the KD‐Tree is used to classify the objects and predict their relationship. After that, a SPARQL query is built to extract a set of similar images. The SPARQL query consists of triple statements describing the objects and their relationship which were previously predicted. The evaluation of the framework with the MS‐COCO dataset and Flickr showed that the precision achieved scores of 0.9218 and 0.9370, respectively. [ABSTRACT FROM AUTHOR]

This paper applies cognitive models, inspired by cognitive science, with the aim to propose architectural and knowledge-based requirements to structure ontological models for the cognitive profiling of agents. The proposed architecture aims to address the lack of flexibility that most agent-based collaborations are affected by. The resulting agents, equipped with advanced cognitive profiling, have an increased cognitive awareness of themselves and are more capable of interacting with other agents in a multi-agents based environment. In this research, cognitive awareness identifies the ability of the web agents to diagnose their processing limitations and to establish interactions with the external environment. The outcome is the enhanced flexibility, reusability and predictability of the agent behaviour; thus contributing towards minimizing human cognitive demands. The concept of cognitive profiling presented in this paper considers the semantic web as an action mediating space, where ontological models provide affordances for improving cognitive awareness through shared knowledge-base. The conceptual model for the cognitive profile architecture is developed with Protégé Ontology editor to generate OWL Ontology and evaluated through a proof of concept. The results show that agents equipped with cognitive awareness can undertake complex tasks more dynamically. [ABSTRACT FROM AUTHOR]

Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems. [ABSTRACT FROM AUTHOR]

According to the latest statistics, the proportion of the elderly (+65) is increasing and is expected to double within the European Union in a period of 50 years. This ageing is due to the improvement of quality of life and advances in medicine in the last decades. Gerontechnology is receiving a great deal of attention as a way of providing the elderly with sustainable products, environments, and services combining gerontology and technology. One of the most important aspects to consider by gerontechnology is the mobility/rehabilitation technologies, because there is an important relationship between mobility and the elderly's quality of life. Telerehabilitation systems have emerged to allow the elderly to perform their rehabilitation exercises remotely. However, in many cases, the proposed systems assist neither the patients nor the experts about the progress of the rehabilitation. To address this problem, we propose in this paper, a fuzzy‐semantic system for evaluating patient's physical state during the rehabilitation process based on well‐known standard for patients' evaluation. Moreover, a tool called FINE has been developed that facilitates the evaluation be accomplished in a semi‐automatic way first asking patients to carry out a set of standard tests and then inferencing their state by means of a fuzzy‐semantic approach using the data captured during the rehabilitation tasks. [ABSTRACT FROM AUTHOR]

Abstract: The problem of improving the efficiency of designing process of knowledge bases and expert systems remains a relevant issue. The effectiveness of this process can be improved by using the principles of conceptual (cognitive) modelling and generative programming‐based approaches, in particular, a model‐driven development. This paper describes implementation and application of the model‐driven development approach for the design of rule‐based expert systems and knowledge bases. The implementation proposed takes into account peculiarities of the intelligent systems development process, such as stages of conceptualization and formalization, and provides the practical use of ontologies and conceptual models as computation‐independent and subject domain models. An extension of UML, namely, the Rule Visual Modelling Language is used to represent logical rules and create platform‐independent and platform‐specific models. Rule Visual Modelling Language elements ensure that some features of a programming language for knowledge bases, for example, CLIPS, are taken into account. Therefore, CLIPS and the Personal Knowledge Base Designer are used as the targeted platforms for generation of source codes and specifications. The applicability of the approach is demonstrated by a case study: development of the knowledge base and expert system for identification of the causes of damages and destruction of construction materials in petrochemistry. [ABSTRACT FROM AUTHOR]

Abstract: Natural language processing is a composition of several error‐prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi‐automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state‐of‐art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks. [ABSTRACT FROM AUTHOR]

This paper describes the development of an architecture for the discovery of sensor services leveraging ontology-based semantics in the search query. A prototype has been implemented based upon the architecture and can be used to support the development of expert system applications in which sensors of certain types, operational capabilities or physical properties are required to support applications within a network-centric environment. In the prototype, sensor services are listed in a registry that references a machine-interpretable ontology. The registry conforms to the Universal Discovery and Description Interface (UDDI) specification, but it is augmented with semantic matching via an ontology to increase the likelihood that relevant sensor services are discovered when needed by expert system applications. [ABSTRACT FROM AUTHOR]

Current approaches to patient education provide generic standardized materials to all patients regardless of their demographics such as age and cognitive abilities. Thus, the effectiveness of this approach may suffer from a patient's motivation to fully engage with the material. To alleviate these concerns, this study proposes a personalized approach to patient education that is tailored to the individual characteristics and health objectives of the patient. Personalized features will enhance the comprehensibility and usability of the process of medical education. Taking this into consideration, this paper introduces a conceptual architecture to create a web-based personalized patient education experience. A key component of this architecture comprises ontological models of the patient themselves, their medical conditions, physical activities and their educational attainments. Furthermore, rule-based reasoning is also proposed to achieve this personalization. A use case scenario is provided to highlight the effectiveness of personalized education provision. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including formal ontology directed to visual data's symbolic representation, visual surveillance systems, and three-dimensional (3D) virtual worlds confluence with social networks.

As software complexity grows and clients demand higher quality software, quality requirements can no longer be considered to be of secondary importance. Thus, eliciting, specifying, prioritizing and validating quality requirements is a prerequisite to the development of effective and efficient information systems. Despite the critical importance of quality requirements, there is a considerable gap in the breadth and depth of quality requirements engineering ( RE) support in most RE approaches. In practice, it is often the case to have quality requirements considered as an afterthought in the systems development process. While there is a wealth of modelling techniques and tools for functional requirements, there is very limited support for quality requirements in RE. Support for quality requirements is usually ad-hoc, without clear guidelines on how to capture, specify and manage quality requirements and also without proper usage of standardized terminologies based on established quality models such as the ISO/IEC 9126 quality model. In this paper, we discuss a quality-driven RE framework and tool that applies knowledge management techniques and quality ontologies to support RE activities. The ontology implements the quality characteristics and metrics prescribed by the ISO/9126 quality model, providing a common vocabulary to address quality concerns/aspects across RE activities. We empirically validate how the framework and tool can be used to effectively support the requirements elicitation and prioritization activities through a case study addressing the development of an intranet portal project at the University of Manchester. [ABSTRACT FROM AUTHOR]

Integration of ontologies of information sources and consumers is an important phase in achieving web-based interoperability. The present work describes an approach for identifying certain semantic conflicts while integrating ontologies of heterogeneous information sources. This paper is focused on the identification of homonymy and synonymy between elements in ontologies. In the present work the concepts of homonymy and synonymy are synonymous to naming conflicts and entity identifier conflicts, respectively, and partial synonymy is synonymous to schema isomorphism conflicts. The concept of the mask of interoperability is introduced for the identification of synonymy. The mask of interoperability is expressed in a declarative way as a set of rules, which can then be used for resolution of conflicts during integration of ontologies. As proof of concept, ontologies are implemented using the XML-based ontology language Ontology Web Language (OWL), and the rules are implemented using the emerging rule language Semantic Web Rule Language (SWRL). This representation in OWL and SWRL allows the ontology to be executable, flexibly extendable and platform-independent. The OWL facts and SWRL rules are used by the Jess and Bossam reasoning engine to identify semantic homonymy and synonymy. [ABSTRACT FROM AUTHOR]

Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model, where the projection (i.e. the main operator for reasoning with conceptual graphs which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs. We also introduce an ontology of representation, called MetaOCGL, dedicated to the reasoning of heavyweight ontologies at the meta-level. [ABSTRACT FROM AUTHOR]

Research on semantic webs has become increasingly widespread in the computer science community. The core technology of a semantic web is an artefact called an ontology. The major problem in constructing an ontology is the long period of time required. Another problem is the large number of possible meanings for the knowledge in the ontology. In this paper, we present a novel ontology construction based on artificial neural networks and a Bayesian network. First, we collected web pages related to the problem domain using search engines. The system then used the labels of the HTML tags to select keywords, and used WordNet to determine the meaningful keywords, called terms. Next, it calculated the entropy value to determine the weight of the terms. After the above steps, the projective adaptive resonance theory neural network clustered the collected web pages and found the representative term of each cluster of web pages using the entropy value. The system then used a Bayesian network to insert the terms and complete the hierarchy of the ontology. Finally, the system used a resource description framework to store and express the ontology results. [ABSTRACT FROM AUTHOR]

The bioscience field has seen some spectacular advances in genomic and proteomic technologies that are able to deliver vast quantities of information on cellular activity. Such technologies are of critical importance to biology, medical science and in drug discovery. However, living systems are highly complex and to fully exploit these technologies requires knowledge at many different levels. Information such as genome sequence data, gene expression data, protein-to-protein interactions and metabolic pathways is required to understand the complexity of biological processes. The challenge for bioinformatics is to tackle the problem of fragmentation of knowledge by integrating the many sources of heterogeneous information into a coherent entity. Another problem is that the high level of biological complexity and the fragmented nature of biological research has meant that it is difficult to keep fully conversant with the latest research and discoveries. Progress in one area of biology may have implications for other areas but the dissemination of this knowledge is not straightforward; difficulties such as differences in naming conventions for genes and biological processes has led to confusion and the lack of productivity. This paper reviews the most recent research to overcome the fragmentation problem where technologies such as text mining and ontologies are used within the knowledge discovery process and the specific technical challenges they address. [ABSTRACT FROM AUTHOR]

The edge computing in knowledge‐defined network (KDN) is a kind of distributed computing architecture, and the format of edge resources stored in different edge computing nodes are different, which yields the data heterogeneity problem and hampers the interaction between edge nodes. Ontology is considered as the solution of data heterogeneity on Semantic Web, and matching ontologies is a high‐efficiency method of addressing the data heterogeneity problem. Ontology meta‐matching investigates how to determine the optimal weights to aggregate multiple similarity measures to achieve high‐quality ontology alignment, which is a challenge about nonlinear mathematical problem in ontology matching domain. To face this challenge, unsupervised learning method such as generative adversarial network (GAN) becomes an effective methodology. GAN consists of two models of different targets that are opposed to each other in training to produce the final best result. To improve the GAN's efficiency, this work further proposes a GAN with simulated annealing algorithm (SA‐GAN), where the stagnation counter is introduced to accelerate GAN's the convergence speed. The experiment uses the famous benchmark in the ontology domain, and the comparisons with the advanced ontology matching systems shows that SA‐GAN is able to find high‐quality alignments to help build bridges between edge nodes on edge computing. [ABSTRACT FROM AUTHOR]

Covid‐19 is an acute respiratory infection and presents various clinical features ranging from no symptoms to severe pneumonia and death. Medical expert systems, especially in diagnosis and monitoring stages, can give positive consequences in the struggle against Covid‐19. In this study, a rule‐based expert system is designed as a predictive tool in self‐pre‐diagnosis of Covid‐19. The potential users are smartphone users, healthcare experts and government health authorities. The system does not only share the data gathered from the users with experts, but also analyzes the symptom data as a diagnostic assistant to predict possible Covid‐19 risk. To do this, a user needs to fill out a patient examination card that conducts an online Covid‐19 diagnostic test, to receive an unconfirmed online test prediction result and a set of precautionary and supportive action suggestions. The system was tested for 169 positive cases. The results produced by the system were compared with the real PCR test results for the same cases. For patients with certain symptomatic findings, there was no significant difference found between the results of the system and the confirmed test results with PCR test. Furthermore, a set of suitable suggestions produced by the system were compared with the written suggestions of a collaborated health expert. The suggestions deduced and the written suggestions of the health expert were similar and the system suggestions in line with suggestions of the expert. The system can be suitable for diagnosing and monitoring of positive cases in the areas other than clinics and hospitals during the Covid‐19 pandemic. The results of the case studies are promising, and it demonstrates the applicability, effectiveness, and efficiency of the proposed approach in all communities. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one on a system for automatic calculation of the quantities of vascular plaque from intravascular ultrasound (IVUS) images, one on new theories, techniques and tools which permit the analysis of vague data sets based on the combination of rough and vague sets theories, and one on how the Semantic Web Rule Language, can be used for executable ontology representations.

The emergence of the Internet of Things (IoT) in the medical field has led to the massive deployment of a myriad of medical connected objects (MCOs). These MCOs are being developed and implemented for remote healthcare monitoring purposes including elderly patients with chronic diseases, pregnant women, and patients with disabilities. Accordingly, different associated challenges are emerging and include the heterogeneity of the gathered health data from these MCOs with ever‐changing contexts. These contexts are relative to the continuous change of constraints and requirements of the MCOs deployment (time, location, state). Other contexts are related to the patient (medical record, state, age, sex, etc.) that should be taken into account to ensure a more precise and appropriate treatment of the patient. These challenges are difficult to address due to the absence of a reference model for describing the health data and their sources and linking these data with their contexts. This article addresses this problem and introduces a semantic‐based context‐aware system (IoT Medicare system) for patient monitoring with MCOs. This system is based on a core domain ontology (HealthIoT‐O), that is, designed to describe the semantic of heterogeneous MCOs and their data. Moreover, an efficient interpretation and management of this knowledge in diverse contexts are ensured through SWRL rules such as the verification of the proper functioning of the MCOs and the analysis of the health data for diagnosis and treatment purposes. A case study of gestational diabetes disease management is proposed to evaluate the effectiveness of the implemented IoT Medicare system. An evaluation phase is provided and focuses on the quality of the elaborated semantic model and the performance of the system. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, flipped learning has received tremendous attention from educational practitioners and researchers. However, this study argues that existing e‐learning systems mainly serve for learning management and content delivery purposes, although they lack support for flipped learning. As an innovative educational approach, flipped learning requires more pedagogical elements, such as integrated instructional design and adaptive content delivery, to achieve effective direct instruction. This study aims to create a learning adaptivity design to support effective learning in the flipped individual learning space in which the teacher is absent. Because teaching involves various pedagogical and content knowledge sources, we propose a conceptual model of teaching as a function of the knowledge triad of Guideline (G), Teaching Activity (T), and Material (M). To realize this conceptualization, an ontological problem‐solving approach is used for knowledge‐based systems development to integrate the relevant knowledge sources. The knowledge model is created using the Protégé platform to develop OWL‐based domain ontology, task ontology, and SWRL‐based semantic rules to enable inference in the GTM triad for learning adaptivity. The case illustration shows that the knowledge‐based system prototype can adaptively guide student learning in the flipped individual learning space with the knowledge sources integrated. [ABSTRACT FROM AUTHOR]

Pressman's (2010) definition of software quality is the conformance to explicitly stated functional and performance requirements, explicitly documented development standards, and implicit characteristics that are expected of all professionally developed software. The achievement of quality is a complex activity that is related with the process quality and product quality. However, still, it is not clear how product quality should apply in the development process. Frequently, the development team does not know what characteristics influence a specific entity and how these characteristics compose the quality of the product. This lack of knowledge reflects the need to define some document that serve as a valid quality specification that should be evaluated along the development process. This contribution addresses this need by introducing an ontology for document a quality scheme based on the product quality model of ISO/IEC 25010. The proposal follows this model with aims to clarify the properties that are normally present in a product and its meaning. The ontology incorporates the metric concept to represent the way in which quality should be measure. The ontology was implemented using Protégé, and it was evaluated using a set of metrics that estimate the required structural characteristics. [ABSTRACT FROM AUTHOR]

Environmental and meteorological conditions are of utmost importance for the population, as they are strongly related to the quality of life. Citizens are increasingly aware of this importance. This awareness results in an increasing demand for environmental information tailored to their specific needs and background. We present an environmental information platform that supports submission of user queries related to environmental conditions and orchestrates results from complementary services to generate personalized suggestions. The system discovers and processes reliable data in the Web in order to convert them into knowledge. At runtime, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference. The platform is demonstrated with real world use cases in the south area of Finland, showing the impact it can have on the quality of everyday life. [ABSTRACT FROM AUTHOR]

Cloud computing, a common business model, provides cloud resources on demand to consumers over the Internet. However, because cloud computing lacks a uniform method of representing knowledge, which can offer customers a comprehensive solution for managing and developing cloud applications, cloud computing has low reuse potential. This work proposes a Semantic Agent as a Service (SAaaS), which was developed using Unified Modeling Language modelling. The SAaaS architecture is based on research into Cloud Computing, Semantic Web and Multi-Agent Systems. The architecture can be combined with existing cloud service models, such as Software as a Service, Platform as a Service and Infrastructure as a Service, to design intelligent cloud computing applications. To demonstrate the efficacy of SAaaS, a Semantic-based Project Resources Sharing Platform, an intelligent cloud computing application based on the SAaaS framework, is implemented to provide project resources on demand, consistent with the needs of project members. [ABSTRACT FROM AUTHOR]

Ontologies are intended to facilitate semantic interoperability among distributed and intelligent information systems. Because of the distributed nature of the World Wide Web, Web ontologies have been developing in multiple forms of heterogeneity. For interoperating among information systems through heterogeneous ontologies, ontology mapping is a prerequisite process to generate alignment between two ontologies. In order to improve alignment accuracy, our approach is to clarify and enrich the semantics of ontological entities before mapping. For this purpose, we present a semi-automatic framework of entity classification and enrichment by applying three philosophical notions: identity condition, existential rigidity, and external dependency. Our objective is to supply a set of philosophy-driven anchors into ontologies for their mapping process by using a sortal taxonomy as a background knowledge model. [ABSTRACT FROM AUTHOR]

Provenanc refers to the origin of information. Therefore, provenance is the metadata that record the history of data. As provenance is the derivation history of an object starting from its original source, the provenance information is used to analyse processes that are performed on an object and to track by whom these processes are performed. Thus, provenance shows the trustworthiness and quality of data. In a provenance management system in order to verify the trustworthy of provenance information, security needs must be also fulfilled. In this work, an ontology‐based privacy‐aware provenance management model is proposed. The proposed model is based on the Open Provenance Model, which is a common model for provenance. The proposed model aims to detect privacy violations, to reduce privacy risks by using permissions and prohibitions, and also to query the provenance data. The proposed model is implemented with Semantic Web technologies and demonstrated for the health care domain in order to preserve patients' privacy. Also, an infectious disease ontology and a vaccination ontology are integrated to the system in order to track the patients' vaccination history, to improve the quality of medical processes, the reliability of medical data, and the decision making in the health care domain. [ABSTRACT FROM AUTHOR]

Computer‐Interpretable Guidelines (CIGs) are the dominant medium for the delivery of clinical decision support, given the evidence‐based nature of their source material. Therefore, these machine‐readable versions have the ability to improve practitioner performance and conformance to standards, with availability at the point and time of care. The formalisation of Clinical Practice Guideline knowledge in a machine‐readable format is a crucial task to make it suitable for the integration in Clinical Decision Support Systems. However, the current tools for this purpose reveal shortcomings with respect to their ease of use and the support offered during CIG acquisition and editing. In this work, we characterise the current landscape of CIG acquisition tools based on the properties of guideline visualisation, organisation, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, we describe the CompGuide Editor, a tool for the acquisition of CIGs in the CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. The Editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition. [ABSTRACT FROM AUTHOR]