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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 describes an architecture for the usage of Instructional Design (ID) knowledge in intelligent instructional systems. In contrast with other architectures, ontologies are used to represent ID knowledge about both what to teach and how to teach. Moreover, set‐theoretic reasoning is used for the provision of inferential services. In particular, the paper shows how set‐theoretic deductions can be applied (i) to support the modelling of ID knowledge bases, (ii) to retrieve suitable teaching methods from them, and (iii) to detect errors in a training design. The intelligent knowledge management environment CONCEPTOOL is used to demonstrate the benefits of the proposed architecture.

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.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots.

For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making.

AuR has identified the core concepts and domains needed to create an ontology for autonomous robots.

AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance.

Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors’knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots.

The purpose of this paper is to make research on causing mechanism of meteorological disaster as well as the components of meteorological disaster system and their semantic relationships. It has important practical significance due to the urgent need of further providing support for pre-assessment of influences of disastrous weather/climate events and promoting the level of emergency management.

This paper analyses the occurrence regulations and components of meteorological disasters and proposes the concept of meta-action. Ontology modelling method is adopted to describe the components and relationships among different parts comprising meteorological disaster system, and semantic web rule language is selected to identify the implicit relationships among the domain knowledge explicitly defined in ontology model. Besides, a case is studied to elaborate how to provide logic and semantic information support for comprehensive risk assessment of disastrous weather/climate events based on rule-based ontology reasoning method. It proves that ontology modelling and reasoning method is effective in providing decision makings.

This paper provides deep analyses about causing mechanisms of meteorological disasters, and implements information fusion of the components of meteorological disaster system and acquisition of potential semantic relations among ontology components and their individuals.

In this paper, on the basis of analysing the disaster-causing mechanisms, the meteorological disaster ontology (MDO) model is proposed by using the ontology modelling and reasoning method. MDO can be applied to provide decision makings for meteorological departments.

The increasingly competitive hotel industry and emerging customer trends where guests are more discerning and want a personalized experience has led to the need of innovative applications. Personalization is much more important for hotels, especially now in the post-COVID lockdown era, as it challenges their business model. However, personalization is difficult to design and realize due to the variety of factors and requirements to be considered. Differences are both in the offer (hotels and their rooms) and demand (customers’ profiles and needs) in the accommodation domain. As for the implementation, critical issues are in hardware-dependent and vendor-specific Internet of Things devices which are difficult to program. Additionally, there is complexity in realizing applications that consider varying customer needs and context via existing personalization options. This paper aims to propose an ontological framework to enhance the capabilities of hotels in offering their accommodation and personalization options based on a guest’s characteristics, activities and needs.

A research approach combining both quantitative and qualitative methods was used to develop a hotel room personalization framework. The core of the framework is a hotel room ontology (HoROnt) that supports well-defined machine-readable descriptions of hotel rooms and guest profiles. Hotel guest profiles are modeled via logical rules into an inference engine exploiting reasoning functionalities used to recommend hotel room services and features.

Both the ontology and the inference engine module have been validated with promising results which demonstrate high accuracy. The framework leverages user characteristics, and dynamic contextual data to satisfy guests’ needs for personalized service provision. The semantic rules provide recommendations to both new and returning guests, thereby also addressing the cold start issue.

This paper extends HoROnt in two ways, to be able to add: instances of the concepts (room characteristics and services; guest profiles), i.e. to create a knowledge base, and logical rules into an inference engine, to model guests’ profiles and to be used to offer personalized hotel rooms. Thanks to the standards adopted to implement personalization, this framework can be integrated into existing reservation systems. It can also be adapted for any type of accommodation since it is broad-based and personalizes varying features and amenities in the rooms.

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.

Pertinent information sharing across various government agencies, as well as non‐governmental and private organizations, is essential to assess the incident situation, identify the needed resources for emergency response and generate response plans. However, each agency may have incident management systems of its choice with valuable information in its own format, posing difficulty in effective information sharing. Application‐to‐application sharing cross agency boundaries will significantly reduce human efforts and delay in emergency response. Information sharing from disparate systems and organizations, however, requires solving of the interoperability issue. The purpose of this paper is to present the UICDS™‐based resource sharing framework as a step toward addressing the afore‐mentioned challenges.

A prototype middleware system is developed using a standards‐based information sharing infrastructure called UICDS™ (Unified Incident Command and Decision Support™), an initiative led by the Department of Homeland Security (DHS) Science and Technology division. This standards‐based middleware, resource management plug‐in utilizes the ontology of organizational structure, workflow activities and resources, and the inference rules to discover and share resource information and interoperability from different incident management applications.

The middleware prototype implementation shows that the UICDS™‐based interoperability between heterogeneous incident management applications is feasible. Specifically, the paper shows that the resource data stored in the Resource Directory Database (RDDB) of the NJ Office of Emergency Management (NJOEM), Hippocrates of the New Jersey Department of Health and Senior Services (NJDHSS) can be discovered and shared with other incident management systems using the ontology and inference rules.

This study illustrates the possible solutions to the application to application interoperability problem using the DHS initiated interoperability platform called UICDS™.

The resource discovery and emergency response planning can be automated using the incident domain ontology and inference rules to dynamically generate the location‐based incident response workflows.