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In successfully meeting city and metropolitan growth, sustainable development is compulsory. Sustainability is a must-focus for any project, particularly for large and mega rail infrastructure. This paper aims to investigate to what degree social, environmental and economic factors influence the government when planning sustainable rail infrastructure projects. To respond to such a matter, this paper focuses on two Australian mega-rail projects: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE).

As the basis of an experimental evaluation framework strengths, weaknesses, opportunities and threats (SWOT) and factor analysis were used. These two methods were specifically selected as comparative tools for SWRL and SWRL projects, to measure their overall sustainability effect.

Using factor analysis, in the MRE, the factors of network capacity, accessibility, employment and urban planning were seen frequently throughout the case study. However, politics and economic growth had lower frequencies throughout this case study. This difference between the high-weighted factors is likely a key element that determined the SWRL to be more sustainable than the MRE. The SWOT analysis showed the strengths the MRE had over the SWRL such as resource use and waste management, and natural habitat preservation. These two analyses have shown that overall, calculating the sustainability levels of a project can be subjective, based on the conditions surrounding various analysis techniques.

This paper first introduces SWRL and MRE projects followed by a discussion about their overall sustainable development. Both projects go beyond the traditional megaprojects' goal of improving economic growth by developing and enhancing infrastructure. Globally, for such projects, sustainability measures are now considered alongside the goal of economic growth. Second, SWOT and factor analysis are undertaken to further evaluate the complexity of such projects. This includes their overall sustainable development vision alignment with environmental, economic and social factors.

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.

Semantic Web is an extension of the World Wide Web by tagging content with “meaning”. In general, question answering systems based on semantic Web face a number of difficult issues. This paper aims to design an experimental environment with custom rules and scalable data sets and evaluate the performance of a proposed optimized backward chaining ontology reasoning system. This study also compares the experimental results with other ontology reasoning systems to show the performance and scalability of this ontology reasoning system.

The authors proposed a semantic question answering system. This system has been built using ontological knowledge base including optimized backward chaining ontology reasoning system and custom rules. With custom rules, the proposed semantic question answering system will be able to answer questions that contain qualitative descriptors such as “groundbreaking” resesarch and “tenurable at university x”. Scalability has been one of the difficult issues faced by an optimized backward chaining ontology reasoning system and semantic question answering system. To evaluate the proposed ontology reasoning system, first, the authors design a number of innovative custom rule sets and corresponding query sets. The innovative custom rule sets and query sets will contribute to the future research on evaluating ontology reasoning systems as well. Then they design an experimental environment including ontologies and scalable data sets and metrics. Furthermore, they evaluate the performance of the proposed optimized backward chaining reasoning system on supporting custom rules. The evaluation results have been compared with other ontology reasoning systems as well.

The proposed innovative custom rules and query sets can be effectively employed for evaluating ontology reasoning systems. The evaluation results show that the scalability of the proposed backward chaining ontology reasoning system is better than in-memory reasoning systems. The proposed semantic question answering system can be integrated in sematic Web applications to solve scalability issues. For light weight applications, such as mobile applications, in-memory reasoning systems will be a better choice.

This paper fulfils an identified need for a study on evaluating an ontology reasoning system on supporting custom rules with and without external storage.

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.

This paper proposes an innovative intelligent simulation-based construction planning framework that introduces a new approach to simulation-based construction planning.

In this approach, the authors developed an ontological inference engine as an integrated part of a constraint-based simulation system that configures the construction processes, defines activities and manages resources considering a variety of requirements and constraints during the simulation. It allows for the incorporation of the latest project information and a deep level of construction planning knowledge in the planning. The construction planning knowledge is represented by an ontology and several semantic rules. Also, the proposed framework uses the project building information model (BIM) to extract information regarding the construction product and the relations between elements. The extracted information is then converted to an ontological format to be useable by the framework.

The authors implemented the framework in a case study project and tested its usefulness and capabilities. It successfully generated the construction processes, activities and required resources based on the construction product, available resources and the planning rules. It also allowed for a variety of analyses regarding different construction strategies and resource planning. Moreover, 4D BIM models that provide a very good understanding of the construction plan can be automatically generated using the proposed framework.

The active integration between BIM, discrete-event simulation (DES) and ontological knowledge base and inference engine defines a new class of construction simulation with expandable applications.

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.

Semantic modelling is an essential prerequisite for designing the intelligent human–computer interaction in future aircraft cockpit. The purpose of this paper is to outline an ontology-based solution to this issue.

The scenario elements are defined considering the cognitive behaviours, system functions, interaction behaviours and interaction situation. The knowledge model consists of a five-tuple array including concepts, relations, functions, axioms and instances. Using the theory of belief-desire-intention, the meta-model of cognitive behaviours is established. The meta-model of system functions is formed under the architecture of sub-functions. Supported by information flows, the meta-model of interaction behaviours is presented. Based on the socio-technical characteristics, the meta-model of interaction situation is proposed. The knowledge representation and reasoning process is visualized with the semantic web rule language (SWRL) on the Protégé platform. Finally, verification and evaluation are carried out to assess the rationality and quality of the ontology model. Application scenarios of the proposed modelling method are also illustrated.

Verification results show that the knowledge reasoning based on SWRL rules can further enrich the knowledge base in terms of instance attributes and thereby improve the adaptability and learning ability of the ontology model in different simulations. Evaluation results show that the ontology model has a good quality with high cohesion and low coupling.

The approach presented in this paper can be applied to model complex human–machine–environment systems, from a semantics-driven perspective, especially for designing future cockpits.

Different from the traditional approaches, the method proposed in this paper tries to deal with the socio-technical modelling issues concerning multidimensional information semantics. Meanwhile, the constructed model has the ability of autonomous reasoning to adapt to complex situations.

With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels.

This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation.

Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper.

The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

Three-dimensional digital design and manufacturing technology are changing the current manufacturing pattern and have become the core of enterprise competition. However, the research and application of three-dimensional digital technology in the present phase have a strong bias toward the design of three-dimensional model and focus little on process planning. It restricts the development of manufacturing industry. Therefore, this paper aims to present a design scheme of three-dimensional digital process planning.

A three-dimensional digital process design method is developed by combining model-based definition technology and knowledge engineering technology. Model-based definition technology is used to display the process information. And knowledge engineering technology is used for process decision; meanwhile, ontology technology is introduced to describe process knowledge. And taking shaft part as an example, this paper establishes the general ontology of manufacturing process and the special ontology of shaft. This research provides an available method for the three-dimensional digital process planning.

Traditional process planning mainly is based on two-dimensional engineering drawing, which leads to the low efficiency and quality of process planning. Moreover, it cannot achieve effective mining and management of knowledge. Thus, applying an effective knowledge management technology into a three-dimensional process system is necessary.

This paper contributes to an available method for three-dimensional digital process planning.

The introduction of model-based definition technology makes process information display in three-dimensional environment. And ontology technology achieves sematic reference and efficient management of process knowledge.

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).