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The objective of this paper is to describe the evolution of the available subject heading list, i.e. the CC Soggettario (Carabinieri Corps Soggettario), towards a thesaurus, that is CCThes (Carabinieri Corps Thesaurus), to support subject indexing and retrieval of the documentary heritage held by the Historical Office of the General Command of the Carabinieri Corps. This work follows the need to implement a controlled vocabulary compliant with the state-of-the-art standards.

The methodology implements the practice of reengineering available vocabularies, following standardised guidelines for thesaurus development. The conversion process includes the balance maintenance of what has been achieved in the CC Soggettario and the enrichment of the semantic structure in the thesaurus by using both deductive and inductive methods.

The main result of this study is a thesaurus compliant with ISO 29964-1:2011 recommendations, which improves information retrieval performances and interoperability with other vocabularies and applications. It generally has a mono-hierarchical structure with the possibility of admitting, as an exception, the poly-hierarchy for a few concepts. An introductive user guide has been created as a complementary tool to the CCThes.

This is an applied study which deals with Knowledge Organisation System (KOS) reengineering and outlines this process using a pragmatic approach. The paper strength lies in providing the description of performed activities and conveying a set of resources to approach KOS reengineering practice. The study is also relevant for the preservation and diffusion of a part of the social memory and identity of Italy.

Exploring diverse knowledge organization systems and metadata schemes in linked data, aiming to promote vocabulary usability and high-quality linked data creation within the LIS field.

We used content analysis to select 77 articles from 13 library and information science journals around our research theme. We identified four dimensions: vocabularies participation, reuse, functions, and naming variations in linked data.

The vocabulary comprises seven main categories and their corresponding 126 vocabularies, which participate in linked data in single, two, and multiple dimensions. These vocabularies are used in the eight LIS subfields. Reusing vocabularies has become integral to linked data publishing, with six categories and their corresponding 66 vocabularies being reused. Ontologies are the most engaged and widely reused category of vocabulary in linked data practice. The mutual support among the three major categories and seven subfunctions of vocabulary promotes the sustainable development of linked data. Under a combination of factors, the phenomenon of terminology name changes and cross-usage between “vocabulary” and “ontology.”

This study has limitations. Although 77 articles on the topic of vocabularies applied in linked data were analyzed and presented with quantitative statistics and visualizations, the exploration of the topic tends to be a practical activity, with limited presence in scholarly articles. Moreover, this study’s analysis of the practical applications of linked data is relatively limited, and the sample literature focused on articles published in English, which may have affected the diversity and inclusiveness of the research sample.

Practically, this study does not confine the application of content analysis solely to the traditional exploration of knowledge organization topics, development trends, or course content. Instead, it integrates the dual perspectives of linked data and vocabularies, employing content analysis to analyze and objectively reveal the application issues of vocabularies in linked data. The conclusions can provide specific guidelines for future applications of vocabularies in the LIS subfields and contribute to promoting interoperability of vocabularies.

This research explores the relationship between linked data and vocabularies, highlighting the diverse manifestations and challenges of vocabularies in linked data. It provides theoretical references for the construction and further development of vocabularies considering technologies such as linked data, drawing attention to the potential and existing issues associated with linked open data vocabularies.

This study extends the application of content analysis to exploring vocabularies, especially Knowledge Organization Systems and metadata schemes in the LIS field linked data, highlighting the mutually beneficial interactions between linked data and vocabularies. It provides guidance for future vocabularies applications in the LIS field and offers insights into vocabularies construction and the healthy development of linked data ecosystems in the era of information technology.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

The purpose of this study is to revisit the conventional wisdom about a key contribution [i.e. strengths, weaknesses, opportunities, threats (SWOT) analysis] in the field of strategic management. The societal context and the role of academics, consultants and executives is taken into account in the emergence of SWOT analysis during the 1960–1980 period as a pivotal development within the broader context of the satisfactory, opportunities, faults, threats (SOFT) approach. The authors report on both the content and the approach, so that other scholars seeking to invigorate indigenous theories and/or underreported strategy practices will thrive.

Applying a historiographic approach, the authors introduce an evidence-based methodology for interpreting historical sources. This methodology incorporates source criticism, triangulation and hermeneutical interpretation, drawing upon insights from robust evidence through three iterative stages.

The underreporting of the SOFT approach/SWOT analysis can be attributed to several factors, including strategy tools being integrated into planning frameworks rather than being published as standalone materials; restricted circulation of crucial long-range planning service/theory and practice of planning reports due to copyright limitations; restricted access to the Stanford Research Institute Planning Library in California; and the enduring popularity of SOFT and SWOT variations, driven in part by their memorable acronyms.

In the spirit of a renaissance in strategic planning research, the authors unveil novel theoretical and social connections in the emergence of SWOT analysis by combining evidence from both theory and practice and delving into previously unexplored areas.

Caution is advised for scholars who examine the discrete time frame of 1960–1980 through mere bibliometric techniques. This study underscores the risks associated with gathering incomplete and/or inaccurate data, emphasizing the importance of triangulating evidence beyond scholarly databases. The paradigm shift of strategic management research due to the advent of large language models poses new challenges and the risk of conserving and perpetuating academic urban legends, myths and lies if training data is not adequately curated.

Effective communication is crucial in the medical field where different stakeholders use various terminologies to describe and classify healthcare concepts such as ICD, SNOMED CT, UMLS and MeSH, but the problem of polysemy can make natural language processing difficult. This study explores the contextual meanings of the term “pattern” in the biomedical literature, compares them to existing definitions, annotates a corpus for use in machine learning and proposes new definitions of terms such as “Syndrome, feature” and “pattern recognition.”

Entrez API was used to retrieve articles form PubMed for the study which assembled a corpus of 398 articles using a search query for the ambiguous term “pattern” in the titles or abstracts. The python NLTK library was used to extract the terms and their contexts, and an expert check was carried out. To understand the various meanings of the term, the contextual environment was analyzed by extracting the surrounding words of the term. The expert determined the appropriate size of the context for analysis to gain a more nuanced understanding of the different meanings of the term pattern.

The study found that the categories of meanings of the term “pattern” are broader in biomedical publications than in common definitions, and new categories have been emerging from the term's use in the biomedical field. The study highlights the importance of annotated corpora in advancing natural language processing techniques and provides valuable insights into the nuances of biomedical language.

The study's findings demonstrate the importance of exploring contextual meanings and proposing new definitions of terms in the biomedical field to improve natural language processing techniques.

The purpose of this study is to investigate a practical strategy for integrating application programming interfaces (APIs) and standard interchange protocols (SIPs) within library and information services. This study will seek to determine how such an integration strategy can improve access to resources, enhance the user experience, optimize library operations and improve the overall efficiency of library services.

A qualitative approach to research will be used in this study. This study will be based on the review of relevant literature sources, case studies and real examples. The data analyzes to determine the practical application of SIP and API integration and identify the major methods, approaches and processes used by libraries to successfully implement integration projects.

This study explores that library and information services may achieve numerous benefits from API and SIP integration. The cases describe how libraries have managed to improve access, user experience, operational efficiency and general performance. Libraries have integrated APIs and SIP to create seamless search experiences, establish communication networks in real-time, and develop automated workflows and customer services. API and SIP integration will transform libraries in future.

The originality of this study is the focus of the API and SIP integration. While other authors have discussed the concept of integration from a theoretical standpoint, this study presents practical recommendations and implementation advice for librarians and researchers. This study uses real cases and examples to illustrate how libraries today have managed to improve their operations with the help of APIs and SIP integration.

This study aims to investigate the role of information normalization in online healthcare consultation, a typical complex human-to-human communication requiring both effectiveness and efficiency. The globalization and digitization trend calls for high-quality information, and normalization is considered an effective method for improving information quality. Meanwhile, some researchers argued that excessive normalization (standardized answers) may be perceived as impersonal, repetitive, and cold. Thus, it is not appreciated for human-to-human communication, for instance, when patients are anxious about their health condition (e.g. with high-risk disease) in online healthcare consultation. Therefore, the role of information normalization in human communication is worthy to be explored.

Data were collected from one of the largest online healthcare consultation platforms (Dxy.com). This study expanded the existing information quality model by introducing information normalization as a new dimension. Information normalization was assessed using medical templates, extracted through natural language processing methods such as Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA). Patient decision-making behaviors, namely, consultant selection and satisfaction, were chosen to evaluate communication performance.

The results confirmed the positive impact of information normalization on communication performance. Additionally, a negative moderating effect of disease risk on the relationship between information normalization and patient decision-making was identified. Furthermore, the study demonstrated that information normalization can be enhanced through experiential learning.

These findings highlighted the significance of information normalization in online healthcare communication and extended the existing information quality model. It also facilitated patient decision-making on online healthcare platforms by providing a comprehensive information quality measurement. In addition, the moderating effects indicated the contradiction between informational support and emotional support, enriching the social support theory.

The purpose of this paper is to propose a taxonomy of artificial intelligence (AI) literacy to support AI literacy education and research.

This study makes use of the facet analysis technique and draws upon various sources of data and information to develop a taxonomy of AI literacy. The research consists of the following key steps: a comprehensive review of the literature published on AI literacy research, an examination of well-known AI classification schemes and taxonomies, a review of prior research on data/information/digital literacy research and a qualitative and quantitative analysis of 1,031 metadata records on AI literacy publications. The KH Coder 3 software application was used to analyse metadata records from the Scopus multidisciplinary database.

A new taxonomy of AI literacy is proposed with 13 high-level facets and a list of specific subjects for each facet.

The proposed taxonomy may serve as a conceptual AI literacy framework to support the critical understanding, use, application and examination of AI-enhanced tools and technologies in various educational and organizational contexts.

The proposed taxonomy provides a knowledge organization and knowledge mapping structure to support curriculum development and the organization of digital information.

The proposed taxonomy provides a cross-disciplinary perspective of AI literacy. It can be used, adapted, modified or enhanced to accommodate education and learning opportunities and curricula in different domains, disciplines and subject areas.

The proposed AI literacy taxonomy offers a new and original conceptual framework that builds on a variety of different sources of data and integrates literature from various disciplines, including computing, information science, education and literacy research.

The purpose of this paper is to consider the interface of research methodology paradigms (which are commonly conventionally perceived as being juxtaposed when conducting research) through the application of textual analytical software.

The argument develops matrices and applies these to the human resource (HR) context of individual and career group sense-making so as to better understand the inherent career dynamics and sense-making in a sample derived from a French media agency context.

The paper’s findings comprise a range of insights from the respondent managers (n = 26). The results suggest that a better understanding of methodologies facilitates the development of theories based on textual data analysis.

The limits of the approach are important. Indeed, it is necessary to make compromises and arrangements with epistemological orthodoxy. Indeed, at first and to be able to process the data with the Alceste© and Tropes©, the interviews were recorded, transcribed and considered as “definitive”. This potentially contradicts the methods of collecting data prescribed by the thematic analysis or the method of cognitive maps. These two interpretive approaches imply a process of co-construction of the results that would not theoretically rely solely on a recording.

It provides insights on how, and to what extent, can data analytical software packages facilitate a better understanding of paradigm (interpretive-positivistic) interoperation and commensurability.

Importantly, the study provides a novel means by which to study the important HR issue of career perception and trajectory.

The methodology-driven approach shows that there is potent scope to map and develop valuable and complementary data perspectives on research issues by interfacing paradigms and textual data analysis tools.

Within the Person-Organization fit framework and Signalling Theory, this study investigates the performance of word dictionaries detecting cultural values in online job advertisements as one form of external communication of an organization. Based upon a merge of the dictionaries, a corporate value analysis of Germany is conducted.

The study builds on a dataset (n > 151 k) of online job advertisements which were scraped from a German job portal. It was pre-processed according to natural language processing standards. For analysing the values of an organization a dictionary based word count was applied. Therefore, the current state-of-the-art dictionaries were tested, and an enhanced dictionary was developed and translated from English to German. Finally, a cluster analysis was conducted.

This study supports the possibility of measuring cultural values in texts where the enhanced dictionary based on Ponitzovskiy shows the best results. It thereby supports the use of the Universal Value Structure model (Schwartz, 1992) as well as the Signalling Theory (Guest et al., 2021), that values spread across 10 core or 4 aggregated dimensions are communicated via online job advertisements. Finally, the study offers a profile of the German corporate culture average as well as 4 cultural clusters and separate organizations, all with different profiles.

This study develops an enhanced dictionary based on a large dataset of online job advertisements for analysing the external communication of values or culture of an organization for improving the Person-Organization fit.