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This paper seeks to develop an ontological approach, in order to make it possible to share a common understanding of accounting theory, in this case, the specific structure of the profit and loss account among people or software agents.

This paper presents an ontology methodology (the Net technique) which represents a semi‐structured element in the domain knowledge of accounting. More specifically, ontology will be used to explain the profit and loss account as a representation of the potential use of this methodology.

To support ontology effectively, a strong accounting information support system in the organization is necessary. The ontology may be used by employees to navigate the information repository of an organization for the effective coordination. In addition, it might be possible for the WWW to be used to generate data, information and knowledge in the accounting domain.

Software agents could extract and aggregate accounting information from numerous web sites, which in turn might answer research questions or be used as input data for other applications.

The development of ontology expands the researcher's ability to generate information by using search methods beyond simple keywords. If only keywords are used in internet searches, then information that is retrieved will often lack the precision necessary for generating quality information. Therefore, in order to retrieve quality information more quickly and accurately, a broader and more extensive ontology development is required.

The paper's aims is to present research that evaluates a technology that assists in organizing and retrieving knowledge stored in a variety of forms (books, papers, models, decision support systems, databases) through a real world application.

Ontology has been used to manage the Water Conservation Digital Library in Florida, USA, which holds a dynamic collection of various types of digital resources in the domain of urban water conservation. The ontology based back‐end powers a fully operational web interface, available at: http://library.conservefloridawater.org Findings – The system has demonstrated numerous benefits of the ontology application, including easier and more precise finding of resources, information sharing and reuse, and has proved to effectively facilitate information management.

A large and dynamic number of concepts makes it difficult to keep the ontology consistent and to accurately manually catalog resources. To address these issues, ongoing research focuses on the area of information extraction with the aid of natural language processing techniques.

The paper presents a real‐world‐verified application of ontology to a digital library. It may be of potential interest for anyone who needs to effectively manage a collection of digital resources.

The purpose of this study was to construct a mind counseling ontology to efficiently facilitate the diagnosis of the diseases of mind. To determine the structure of mind counseling ontology, this study conducted analysis on structural forms available in counseling books and other related fields and adopted essential ones in the explanation of counseling. The processing of the diseases of mind was divided into three stages: cause, symptoms and counseling. The stages were analyzed one by one in terms of process, functional elements and relevant technique necessary at each stage.

In the mind counseling list, there are 12 different diagnoses of diseases of mind that are classified into four classes. Thus, the causes, symptoms, prescription and medical history for 12 diseases of mind are defined as a higher rank concept of mind counseling ontology. The causes, symptoms, prescription and medical history consist of definition, affective characteristics and related factors, while the potential diagnosis consists of definition and risk factor. This information does specify detailed notions in the diagnosis of diseases of mind, but considering the limitation of not being able to represent all the diseases, this study enables a counseling center to give and use individual definitions of diagnostic terminology of their own.

This study adopted the top-down approach, in which mind counseling ontology defines a higher rank concept, the terminology in diagnosing diseases of mind, based on the list of terms from the counseling record that specifies the abstract concepts of the diagnosis. The bottom-up approach was also incorporated, which defines the diagnostic terms extracted from the counseling record as a subordinate concept of the mind counseling ontology. Thus, the development of the mind counseling ontology involves the combination of top-down and bottom-up approaches to the construction of ontology.

This research has significance in that it deals with the fundamental problem of the mind aiming for a true change and healing of it, which is the ultimate purpose of this ontology, especially in the circumstances where research on ontology in diagnosing the diseases of mind is unprecedented.

Hybrid cloud computing is considered a viable and cost-effective approach to satisfy the inability of a private cloud to meet the user requirements. The information status of the selected public cloud service may change at runtime which should be reflected at the broker server database. This research illustrates a mechanism to assist the IaaS discovery system to assess the status change of the selected public cloud service and to update the broker server database. The paper aims to discuss these issues.

A prototype was developed with the broker server as the main component in the service discovery system containing the status information of the selected public cloud service. The merge-ontology and patch-update methods were proposed, the processing cost details of each were measured, and the methods were evaluated.

Experimental results showed that in the merge update, the merging process incurred much longer time than its required communication, contributing to long overall time. Relatively, the patch update method incurred much less time than its counterpart.

The proposed mechanism is experimental with some ideal assumptions, and so further work in real conditions is needed for its improvement.

This research is believed to be the first proposal to investigate ontology merge/patch methods to support ontology update in the broker server database of a hybrid cloud and will serve as a reference to researchers in the field.

Ontologies play an important role in enabling machines to understand and navigate a knowledge base. Currently, a number of ontologies covering specific topic in the Quran have been created to serve a particular application. However, these existing Quran ontologies are limited in scope and knowledge. Specifically, existing ontologies do not support contextual information that is considered necessary for correct interpretation of the verses of Quran. The paper aims to discuss these issues.

In order to fill this gap, this paper reports development of an ontology for Juz' Amma that encapsulates contextual information support, which are the translations, revelations place, tafsir, and hadiths.

The developed ontology was evaluated and was found to satisfy the requirements specification.

In addition, this ontology can be reused and further enhanced to support many Quran-related semantic applications in the future.

The purpose of this paper is to integrate distributed ontologies on the web system and clarify the structure of the integrated one.

A formal method based on concept lattices is introduced as a mechanism to form more general semantic level. By checking the extension and the intension of concept, this method extracts the concept pairs satisfying inclusion relations from descartes' set of concepts in distributed ontologies first, and then constructs a concept lattice according to these concept pairs. An algorithm to reduce redundant relations is also proposed to clarify the structure of integrated ontology.

The experiments demonstrate the effectiveness of the proposed method to reduce redundant relations, and the Nir‐to‐Ncr ratio inclines to 1.05 from 3.13.

Instances of certain concept are not given completely on the web, so it is difficult to check extension of different concepts.

A very useful method of integrating distributed ontologies on the web.

Compared with existing methods, this formal method can be performed by program automatically without any human intervening, and can extract the inclusion relations between concepts from distributed ontologies completely.

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

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.

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.

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.