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The purpose of this paper is to construct a structural definition-based terminology ontology system that defines the meanings of academic terms on the basis of properties and links terms with properties that are structured by conceptual categories (classes). This study also aims to test the possibility of semantic searches by generating inference rules and setting very complicated search scenarios.

For the study, 55,236 keywords from the articles of the “Korea Citation Index” were structurally defined and relationships among terms and properties were built. Then, the authors converted the RDB data into RDF and designed ontologies using the ontology developing tool Protégé. The authors also tested the designed ontology with the inference engine of the Protégé editor. The generated reference rules were tested by TBox and SPARQL queries.

The authors generated inference control rules targeting high-input-ratio data in the properties of classes by calculating the input ratio of real input data in the system, and then the authors executed a semantic search by SPARQL query by setting very complicated search scenarios, for which it would be difficult to deduce results via a simple keyword search. As a result, it was confirmed that the search results show the logical combination of semantically related term data.

The proposed terminology ontology system was constructed with the author keywords from research papers, it will be useful in searching the research papers which include the keywords as search results by the complex combination of semantic relation. And the Structural Terminology Net database could be utilized as an index database in retrieval services and the mining of informal big data through the application of well-defined semantic concepts to each term.

This paper presented a methodology for supporting IR using expanded queries based on a novel model of structural terminology-based ontology. The user who wants to access the specific topic can create query that brings the semantically relevant information. The search results show the logical combination of semantically related term data, which would be difficult to deduce results via traditional IR systems.

Institutional repositories (IR) are usually used to archive and manage digital collections including research results, educational material, etc. Learning management systems (LMS) form a popular basis for e-learning and blended learning. This paper aims to study how to integrate IR and LMS to support accessibility of disabled students and students with learning difficulties (dyslexic students) in higher education. Customised ontologies focusing on disabled students can be used to facilitate indexing, and access of items in the repository.

The authors propose a simple methodological approach to establish an integrating system for supporting accessibility. First, the authors review research works related to adaptive learning environments (ALEs) and blended learning, and discuss issues of the interoperability of IR and LMS. Then, based on the review, the authors discuss the use of an integrated ALE for supporting disabled students in the domain of higher technological education. The integrated system is based on IR, LMS and assistive and adaptive technology. The open source software platform DSpace is used to build up the repository applications Use of the web ontology language (OWL) ontologies is also proposed for indexing and accessing the various, heterogeneous items stored in the repository. Various open source LMS (e.g. openeclass) could be used to build up the integrated system. Finally, the authors describe experimentation with a prototype implemented to provide the mentioned capabilities.

The technology is mature enough for building up integrated systems, combining capabilities of IR and LMS, for supporting disabled students. The use of ontologies focused on disabled students could facilitate the use of such integrated systems. Customisation and operation of a platform, for the selection and use of portions of OWL ontologies, could be based on the open source software Protégé. Such a platform forms a basis to create an appropriate ontology suitable for specific domains, e.g. the domain of technological education. Finally, the authors argue that the combined use of the OWL platform and the DSpace repository with open source LMS platforms could support domain experts for creating customised ontologies and facilitating searching.

A new perception of the term integrated system for supporting disabled students in the higher education context is presented. This perception tries to combine the IR technology that supports the self-archiving approach of information, open LMS technology and the user-centred approach to support students and manage the “life of information”.

This study aims to provide an ontology based Baysian network for clinical specialty supporting. As a knowledge base, ontology plays an essential role in domain applications especially in expert systems. Interactive question answering systems are suitable for personal domain consulting and recommended for real-time usage. Clinical specialty supporting for dispatching patients can assist hospitals to locate desired treatment departments for individuals relevant to their syndromes and disease efficiently and effectively. By referring to interactive question answering systems, individuals can understand how to alleviate time and medical resource wasting according to recommendations from medical ontology-based systems.

This work presents an ontology based on clinical specialty supporting using an interactive question answering system to achieve this aim. The ontology incorporates close temporal associations between words in input query to represent word co-occurrence relationships in concept space. The patterns defined in lexicon chain mechanism are further extracted from the query words to infer related concepts for treatment departments to retrieve information.

The precision and recall rates are considered as the criteria for model optimization. Finally, the inference-based interactive question answering system using natural language interface is adopted for clinical specialty supporting, and indicates its superiority in information retrieval over traditional approaches.

From the observed experimental results, we find the proposed method is useful in practice especially in treatment department decision supporting using metrics precision and recall rates. The interactive interface using natural language dialogue attracts the users’ attention and obtains a good score in mean opinion score measure.

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 current work provides a framework for the ranking of ontology development methodologies (ODMs).

The framework is a step-by-step approach reinforced by an array of ranking features and a quantitative tool, weighted decision matrix. An extensive literature investigation revealed a set of aspects that regulate ODMs. The aspects and existing state-of-the-art estimates facilitated in extracting the features. To determine weight to each of the features, an online survey was implemented to secure evidence from the Semantic Web community. To demonstrate the framework, the authors perform a pilot study, where a collection of domain ODMs, reported in 2000–2019, is used.

State-of-the-art research revealed that ODMs have been accumulated, surveyed and assessed to prescribe the best probable ODM for ontology development. But none of the prevailing studies provide a ranking mechanism for ODMs. The recommended framework overcomes this limitation and gives a systematic and uniform way of ranking the ODMs. The pilot study yielded NeOn as the top-ranked ODM in the recent two decades.

There is no work in the literature that has investigated ranking the ODMs. Hence, this is a first of its kind work in the area of ODM research. The framework supports identifying the topmost ODMs from the literature possessing a substantial amount of features for ontology development. It also enables the selection of the best possible ODM for the ontology development.

The purpose of this paper is to provide a literature review of the principal formats and frameworks that have been used in the last 20 years to exchange linguistic resources. It aims to give special attention to the most recent approaches to publishing linguistic linked open data on the Web.

Research papers published since 1990 on the use of various formats, standards, frameworks and methods to exchange linguistic information were divided into two main categories: those proposing specific schemas and syntaxes to suit the requirements of a given type of linguistic data (these are referred to as offline approaches), and those adopting the linked data (LD) initiative and the semantic web technologies to support the interoperability of heterogeneous linguistic resources. For each paper, the type of linguistic resource exchanged, the framework/format used, the interoperability approach taken and the related projects were identified.

The information gathered in the survey reflects an increase in recent years in approaches adopting the LD initiative. This is due to the fact that the structural and syntactic issues which arise when addressing the interoperability of linguistic resources can be solved by applying semantic web technologies. What remains an open issue in the field of computational linguistics is the development of knowledge artefacts and mechanisms to support the alignment of the different aspects of linguistic resources in order to guarantee semantic and conceptual interoperability in the linked open data (LOD) cloud. Ontologies have proved to be of great use in achieving this goal.

The research presented here is by no means a comprehensive or all‐inclusive survey of all existing approaches to the exchange of linguistic resources. Rather, the aim was to highlight, analyze and categorize the most significant advances in the field.

This survey has practical implications for computational linguists and for every application requiring new developments in natural language processing. In addition, multilingual issues can be better addressed when semantic interoperability of heterogeneous linguistic resources is achieved.

The paper provides a survey of past and present research and developments addressing the interoperability of linguistic resources, including those where the linked data initiative has been adopted.

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.

This paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.

A design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.

The study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.

The originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

Health-care ontologies and their terminologies play a vital role in knowledge representation and data integration for health information. In health-care systems, Internet of Technology (IoT) technologies provide data exchange among various entities and ontologies offer a formal description to present the knowledge of health-care domains. These ontologies are advised to assure the quality of their adoption and applicability in the real world.

Ontology assessment is an integral part of ontology construction and maintenance. It is always performed to identify inconsistencies and modeling errors by the experts during the ontology development. A smart health-care ontology (SHCO) has been designed to deal with health-care information and IoT devices. In this paper, an integrated approach has been proposed to assess the SHCO on different assessment tools such as Themis, Test-Driven Development (TDD)onto, Protégé and OOPs! Several test cases are framed to assess the ontology on these tools, in this research, Themis and TDDonto tools provide the verification for the test cases while Protégé and OOPs! provides validation of modeled knowledge in the ontology.

As of the best knowledge, no other study has been presented earlier to conduct the integrated assessment on different tools. All test cases are successfully analyzed on these tools and results are drawn and compared with other ontologies.

The developed ontology is analyzed on different verification and validation tools to assure the quality of ontologies.