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Through examination of the Library Reference Model (LRM) specifications for nomen and the potential challenges visual nomen might present for their description and use in information systems, the purpose of this study was to investigate two questions: (1) how do nonlinguistic or nonalphanumeric signs or symbols act as nomen to identify entities? and (2) what details or attributes are relevant to describe and classify such nomen to integrate them into information systems?

This research was built on an exploratory, qualitative instrumental case study design using multiple (or comparative) cases. Using the International Federation of Library Associations and Institutions LRM conceptualization of nomen as the basis, this research explored the similarities and differences between the LRM definition, its attributes and the use of nonlinguistic and nonalphanumeric “strings” for visual nomen to represent a res, moving iteratively between the LRM documentation, visual nomen identified in previous research and additional examples. This study used a constant comparative method to conduct a structured, focused comparison across different cases found in the source survey.

A close review of the history of the development of the nomen entity was made to understand the semiotic relationship between entities and their symbolic representation, how those symbols are then reified to be further classified and described and how such definitions in the LRM offer a path forward for better understanding the role and function of visual nomen. Based on the foundation of the nomen entity and its attributes established in the LRM, this research then looked at visual representations of concepts and entities to suggest a nascent framework for describing aspects of visual nomen which may be relevant to their use and application

This exploratory study of the use of supralinguistic ways of referencing entities delineates novel insights into a potential framework for describing and using visual nomen as a way of labeling or naming entities represented in information systems. By examining the specifications of the nomen entity and its attributes as delineated by the LRM, this study reinforces the applicability of LRM-defined attributes in the use of visual nomen in addition to offering other attributes or dimensions.

The purpose of this study is to develop a conceptual model, ProvKOS, for tracking the provenance of change activities in a knowledge organization system (KOS). By extending current provenance practices, this model represents dynamic changes in a KOS more effectively.

We take a five-step approach to develop the conceptual model, including content analysis of KOS editorial data, environmental scan of existing provenance models, development of persona-specific provenance questions and a participatory design with stakeholders to ensure the model’s utility.

We introduce (1) a taxonomy of editorial activities for a KOS; (2) a conceptual model ProvKOS, which extends existing models PROV and Simple Knowledge Organization Systems (SKOS). We also provide detailed data dictionaries for the entities, activities and warrants classes proposed in the model. A use case on “gender dysphoria” in Dewey Decimal Classifications (DDCs) is provided to illustrate the implementation of ProvKOS. This shows ProvKOS’s ability to capture KOS changes effectively and to link external resources relating to the changes.

Further validation may be needed to implement the ProvKOS model across various types of KOSs.

ProvKOS can help improve machine readability, querying and analysis of a KOS. Especially within the linked data environment, the enhanced provenance documentation through ProvKOS can enable a network of KOSs, which will then inform better linked data or knowledge graph designs.

By facilitating better tracking of changes within a KOS and across KOSs, ProvKOS can enhance the accessibility and usability of knowledge bases across different cultural and social contexts, thus better supporting inclusive information practices.

The proposed model is novel in two ways: one, its ability to represent dynamic change activities in a KOS, which has not been discussed anywhere else; two, it supports the interconnectivity across KOSs by providing a “warrant” class to substantiate the context of changes.

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.

Knowledge organization (KO) has been advancing at a progressively rapid pace under the influence of information technology. This study aims to explore the topics, characteristics, and trends of KO research in the 21st century.

The full text of 4,360 KO-related articles published from 2000 to 2021 is collected. Through content analysis, this study identifies the topics, research methods, and application areas of each article, and the statistics are presented through a series of visualizations.

In total, 13 main topics, 105 sub-topics, 16 research methods, and 57 application areas are identified. Notably, classification has always been an important topic, while linked data, automated techniques, and ontology have become popular topics recently. Significant changing features have also occurred. The versatile use of research methods has increased, with empirical research becoming the mainstream. Application areas show a trend of refinement from subject areas to specific scenarios. Construction techniques present a combination of automated techniques, crowdsourcing, and experts.

KO has evolved and diversified due to technological developments. This study is the first to focus on the continuous changing features over an extended, 21-year period, as opposed to sampling a few years. It also provides clues and insights for researchers and practitioners interested in KO to understand how it has changed in the Semantic Web and big data context.

The purpose of this paper is to determine the future of the tourism industry in Haiti. More specifically, the paper answers the following question: will Haiti be able to reclaim a positive image and leading position in the Caribbean as a tourist destination?

Within the paradigm of theory building and exploratory approach, this conceptual study is based on a narrative literature review.

The turning point in the development of the tourism industry in Haiti has been the 2010 earthquake which has triggered a will to provide quality products and service specifically in the hospitality sector, the most dynamic sector of the tourism industry. With the diaspora, Haiti has the potential to reclaim a positive image and a leading position in the Caribbean. That said, before performing at this level, the destination must first and foremost contribute to the wellbeing of its people as a sine qua non condition for the success of its tourism industry.

The findings of this research may help potential investors to decide whether or not they want to invest in Haiti. The findings of the paper may also assist the DMO in its branding and marketing strategy.

The alleviation of poverty using tourism as a tool in a post-colonial, post-conflict and post-disaster context should be analysed, understood and approached from a human aspect point of view and perspective. Resilience is what better describes the tourism industry and the locals in Haiti. The locals are neither passive nor powerless.

The coronavirus disease 2019 (COVID-19) pandemic has subjected a considerable strain on the healthcare (HC) systems around the world. The most affected countries are developing countries because of their weak HC infrastructure and meagre resources. Hence, building the resilience of the HC system of such countries becomes essential. Therefore, this study aims to build a resilience-based model on the HC sector of Pakistan to combat the COVID-19 and future pandemics in the country.

The study uses a novel hybrid approach to formulate a model based on resilient attributes (RAs) and resilient strategies (RSs). In the first step, the multi-criteria decision-making (MCDM) technique, i.e. full consistency method (FUCOM) is used to prioritize the RAs. Whereas, the fuzzy quality function deployment (QFD) is used to rank the RSs.

The findings suggest “leadership and governance capacity” to be the topmost RA. Whereas “building the operational capacity of the management”, “resilience education” and “Strengthening laboratories and diagnostic systems” are ranked to be the top three RSs, respectively.

The model developed in this study and the prioritization RAs and RSs will help build resilience in the HC sector of Pakistan. The policymakers and the government can take help from the prioritized RAs and RSs developed in this study to help make the current HC system more resilient towards the current COVID-19 and future pandemics in the country.

A new model has been developed to present a sound mathematical model for building resilience in the HC sector consisting of FUCOM and fuzzy QFD methods. The main contribution of the paper is the presentation of a comprehensive and more robust model that will help to make the current HC system of Pakistan more resilient.

This narrative review presents a multi-stakeholder ethical framework for AI-augmented HRM, based on extant research in the domains of ethical HRM and ethical AI. More specifically, the authors identify critical ethical issues pertaining to AI-augmented HRM functions and suggest ethical principles to address these issues by identifying the relevant stakeholders based on the responsibility ethics approach.

This paper follows a narrative review approach by first identifying various ethical/codes/issues/dilemmas discussed in HRM and AI. The authors next discuss ethical issues concerning AI-augmented HRM, drawing from recent literature. Finally, the authors propose ethical principles for AI-augmented HRM and stakeholders responsible for managing those issues.

The paper summarises key findings of extant research in the ethical HRM and AI domain and provides a multi-stakeholder ethical framework for AI-augmented HRM functions.

This research's value lies in conceptualising a multi-stakeholder ethical framework for AI-augmented HRM functions comprising 11 ethical principles. The research also identifies the class of stakeholders responsible for identified ethical principles. The research also presents future research directions based on the proposed model.

Blockchain technology, a key feature of the fourth industrial revolution, is receiving widespread attention and exploration around the world. Taking the coronavirus pandemic as an example, the purpose of this study to examine the application of blockchain technology from the perspective of epidemic prevention and control.

Exploring multiple case studies in the Chinese context at various stages of deployment, this study documents a framework about how some of the major challenges associated with COVID-19 can be alleviated by leveraging blockchain technology.

The case studies and framework presented herein show that utilization of blockchain acts as an enabler to facilitate the containment of several COVID-19 challenges. These challenges include the following: complications associated with medical data sharing; breaches of patients' data privacy; absence of real-time monitoring tools; counterfeit medical products and non-credible suppliers; fallacious insurance claims; overly long insurance claim processes; misappropriations of funds; and misinformation, rumors and fake news.

Blockchain is ushering in a new era of innovation that will lay the foundation for a new paradigm in health care. As there are currently insufficient studies pertaining to real-life case studies of blockchain and COVID-19 interaction, this study adds to the literature on the role of blockchain technology in epidemic control and prevention.