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The purpose of this paper is to explore the feasibility that an ontological approach can be applied to formalize the construction regulation constraint knowledge in a computer-interpretable way, for construction quality checking, during construction stage.

The ontological and semantic web technologies are used to model the construction quality constraints knowledge into Axioms/OWL and SWRL rules. Protégé platform is selected to illustrate how the construction quality checking, based on the Axioms/OWL and SWRL rules, is achieved.

The ontology and semantic web technologies can be an alternative way for modeling the construction regulation constraints in a computer-interpretable way, and can be implemented for the regulation-based construction quality checking.

The approach is illustrated only with given specific technical constraints examples, the generality and practicality of the approach need further investigation.

The paper introduces an ontological and semantic approach to model and formalize the construction regulation constraints for construction quality checking, and proves the feasibility by the case studies. The proposed approach enables the regulations can be understood and retrieved semantically by computers, which facilitates the using of regulation codes.

Erythemato-squamous disease (ESD) is one of the complex diseases related to the dermatology field. Due to common morphological features, the diagnosis of ESDs become stringent and leads to inconsistency. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for ESD is essential to ensure credibility, consistency, to resolve lack of time, labor and competence and to diminish human error.

This paper presents the design of an automatic ontology framework through data mining techniques and subsequently depicts the diagnosis of ESD using the available knowledge- and rule-based system.

The rule language (Semantic Web Rule Language) and rule engine (Jess and Drools) have been integrated to explore the severity of the ESD and foresee the most appropriate class to be suggested.

In this paper, the authors identify the efficiency of the rule engine and investigate the performance of the computational techniques in predicting ESD using three different measures.

Primarily, the approach assesses transfer time for total number of axioms exported to rule engine (Jess and Drools) while the other approach measures the number of inferred axioms (process time) using the rule engine while the third measure calculates the time to translate the inferred axioms to OWL knowledge (execution time).

The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.

To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.

An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.

The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.

To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.

The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.

Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.

An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

As international users increase rapidly, multilingual systems have become a very important service for global users. The purpose of this paper is to design and implement an ontology‐driven medical information retrieval (OMIR) system by building a medical ontology based on the Centers for Disease Control and Prevention's (CDC) medical records.

A traditional cataloging scheme is used as a navigation menu in the CDC system. This traditional cataloging scheme is transformed to a unique medical ontology for global users in the OMIR system. An experimental study was conducted on both an ontology‐driven medical information system (OMIR) and the CDC system.

The medical ontology can be used to filter out unsuitable resources based on semantic relationships. In addition, the recommended resources can be categorized and provide the patron with different languages to access resources. The OMIR system provides better relevancy and shorter search times compared with alternative systems.

The OMIR system is currently implemented for medical resources from the CDC. The developed method may also be applied to other domain areas.

This paper represents a practical method of building a multilingual medical information retrieval system and explains the functional use of ontological knowledge. This study provides insights into medical information seeking performance on the medical database systems.

Affordable mobile devices with video playback functionality are rapidly growing in the market. Current wireless and third generation communication networks enable smoother and higher quality streaming video. With the support of these technologies, most participants in telecom value‐added service chain are planning to shift their business focus to a more profitable and appealing area, mobile TV. Previous work that survey on users' behavior while consuming mobile TV has indicated that users normally watch brief and casual contents, and not the full program. However, most of the current services adopt a “push” approach since users passively receive pre‐defined contents, rather than pulling the interesting topics and segments. In order to promote a more enjoyable and rewarding watching experience, this paper will propose a framework to support a fully interactive mobile TV. The main goal is to enable users to: 1) visually locate interesting topics across multiple genres (such as news, sports and entertainment) and 2) fully control the playback flow of the multimedia items while selecting the most interesting segments. A web‐based system has been developed to implement and test the effectiveness of the proposed framework in a wireless and mobile setting.

The purpose of this paper is to demonstrate how the semantic rules in conjunction with ontology can be applied for inferring new facts to dispatch news into corresponding departments.

Under a specific task domain, the proposed design comprises finding a glossary from electronic resources, gathering organization functions as controlled vocabularies, and linking relationships between the glossary and controlled vocabularies. Web ontology language is employed to represent this knowledge as ontology, and semantic web rule language is utilized to infer implicit facts among instances.

Document dispatching is highly domain dependent. Human perspectives being adopted as predefined knowledge in understanding document meanings are important. Knowledge‐intensive approaches such as ontology can model and represent expertise as reusable components. Ontology and rules together extend inference capabilities in semantic relationships between instances.

Empirical lessons reveal that ontology with semantic rules can be utilized to model human subjective judgement as knowledge bases. An example, including ontology and rules, based on news dispatching is provided.

An organization can classify and deliver documents to corresponding departments based on known facts by following the described procedure.

Supply chain management has profited tremendously from the advance of Web technologies during the last decade. New applications as well as optimized existing applications have used the Web as the medium to provide efficient and effective services to internal and external company users. The new paradigm in the universal World Wide Web is the semantic web, where the use of ontologies allows for knowledge management in ways that were not possible before. The supply chain management sector can benefit immensely by moving toward such technologies. The basic elements of a prototype knowledge management system for supply chain management are presented in this paper. In these basic characteristic sets of ontologies we examine how the semantic web can provide considerable improvements over previously used paradigms.

The purpose of this paper is to describe the CODO ontology (COviD-19 Ontology) that captures epidemiological data about the COVID-19 pandemic in a knowledge graph that follows the FAIR principles. This study took information from spreadsheets and integrated it into a knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph.

The knowledge graph was designed with the Web Ontology Language. The methodology was a hybrid approach integrating the YAMO methodology for ontology design and Agile methods to define iterations and approach to requirements, testing and implementation.

The hybrid approach demonstrated that Agile can bring the same benefits to knowledge graph projects as it has to other projects. The two-person team went from an ontology to a large knowledge graph with approximately 5 M triples in a few months. The authors gathered useful real-world experience on how to most effectively transform “from strings to things.”

This study is the only FAIR model (to the best of the authors’ knowledge) to address epidemiology data for the COVID-19 pandemic. It also brought to light several practical issues that generalize to other studies wishing to go from an ontology to a large knowledge graph. This study is one of the first studies to document how the Agile approach can be used for knowledge graph development.

This work provides a generic review of the existing data mining ontologies (DMOs) and also provides a base platform for ontology developers and researchers for gauging the ontologies for satisfactory coverage and usage.

The study uses a systematic literature review approach to identify 35 DMOs in the domain between the years 2003 and 2021. Various parameters, like purpose, design methodology, operations used, language representation, etc. are available in the literature to review ontologies. Accompanying the existing parameters, a few parameters, like semantic reasoner used, knowledge representation formalism was added and a list of 20 parameters was prepared. It was then segregated into two groups as generic parameters and core parameters to review DMOs.

It was observed that among the 35 papers under the study, 26 papers were published between the years 2006 and 2016. Larisa Soldatova, Saso Dzeroski and Pance Panov were the most productive authors of these DMO-related publications. The ontological review indicated that most of the DMOs were domain and task ontologies. Majority of ontologies were formal, modular and represented using web ontology language (OWL). The data revealed that Ontology development 101, METHONTOLOGY was the preferred design methodology, and application-based approaches were preferred for evaluation. It was also observed that around eight ontologies were accessible, and among them, three were available in ontology libraries as well. The most reused ontologies were OntoDM, BFO, OBO-RO, OBI, IAO, OntoDT, SWO and DMOP. The most preferred ontology editor was Protégé, whereas the most used semantic reasoner was Pellet. Even ontology metrics for 16 DMOs were also available.

This paper carries out a basic level review of DMOs employing a parametric approach, which makes this study the first of a kind for the review of DMOs.