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The present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.

The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.

The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.

Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.

This exploratory study aims to help contribute to the understanding of online information search behaviour of PhD students from different humanities fields, with a focus on subject searching.

The methodology is based on a semi-structured interview within which the participants are asked to conduct both a controlled search task and a free search task. The sample comprises eight PhD students in several humanities disciplines at Linnaeus University, a medium-sized Swedish university from 2020.

Most humanities PhD students in the study have received training in information searching, but it has been too basic. Most rely on web search engines like Google and Google Scholar for publications' search, and university's discovery system for known-item searching. As these systems do not rely on controlled vocabularies, the participants often struggle with too many retrieved documents that are not relevant. Most only rarely or never use disciplinary bibliographic databases. The controlled search task has shown some benefits of using controlled vocabularies in the disciplinary databases, but incomplete synonym or concept coverage as well as user unfriendly search interface present hindrances.

The paper illuminates an often-forgotten but pervasive challenge of subject searching, especially for humanities researchers. It demonstrates difficulties and shows how most PhD students have missed finding an important resource in their research. It calls for the need to reconsider training in information searching and the need to make use of controlled vocabularies implemented in various search systems with usable search and browse user interfaces.

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.

This article aims to help ensure high-quality subject access to Swedish lesbian, gay, bisexual, transgender, queer and intersexual (LGBTQI) fiction, and aims to identify challenges that librarians consider important to address, on behalf of themselves and end users.

A web-based questionnaire comprising 35 closed and open questions, 22 of which were required, was sent via online channels in January 2022. By the survey closing date, 20 March 2022, 82 responses had been received. The study was intended to complement an earlier study targeting end users.

Both this study of librarians and the previous study of end users have painted a dismal image of online search services when it comes to searching for LGBTQI fiction. The need to consult different channels (e.g. social media, library catalogues and friends), the inability to search more specifically than for the broad LGBTQI category and suboptimal search interfaces were among the commonly reported issues. The results of these studies are used to inform the development of a dedicated Swedish LGBTQI fiction database with an online search interface.

The subject searching of fiction via online services is usually limited to genre with facets for time and place, while users are often seeking characteristics such as pacing, characterization, storyline, frame/setting, tone and language/style. LGBTQI fiction is even more challenging to search because indexing practices are not really being standardized or disseminated worldwide. This study helps address this important gap, in both research and practical applications.

This study aims to collect distributed knowledge organization systems (KOSs) from various domains, enrich each with meta information and link them to the multilingual KOS registry, facilitating integrated search alongside KOSs from various languages and regions.

This research involved collecting and organizing KOS information through three primary steps. The initial phase involved finding KOSs from Web search results, supplemented by the Korea ON-line E-Procurement System (KONEPS) and the National R&D Integrated Notification Service. After obtaining these KOSs, they were enriched by structuring contextual meta information using Basic Register of Thesauri, Ontologies and Classification (BARTOC) metadata elements and established dedicated media wiki pages for each. Finally, the KOSs were linked to the multilingual KOS registry, BARTOC, ensuring seamless integration with KOSs from various languages and regions and creating connections between each registry entry and its associated KOS wiki page.

The research findings revealed several insights, as follows: (1) importance of a stable source for collecting KOS: no national body currently oversees KOS registration, underscoring the need for a systematic approach to collect dispersed KOSs. For Korean KOSs (K-KOSs), KONEPS and National R&D Integrated Notification Service are effective data sources. (2) Importance of enhanced metadata: merely collecting KOSs were not enough. Enhanced metadata bridges access gaps and dedicated wiki pages aid user identification and understanding. (3) Observations from multilingual registry uploads: When adding KOSs to a multilingual registry, similarities were observed across languages and regions. Recognizing this, the K-KOSs were linked with their international counterparts, fostering potential global collaboration.

Due to the absence of a dedicated KOS registry agency, the study might have missed KOSs from certain fields or potentially over-collected from others. Furthermore, this study primarily focused on K-KOSs and their integration into the BARTOC registry, which might influence the methods and perspectives on collecting and establishing links among analogous KOSs in the registry.

This research pursued a stable method to detect KOS development and revisions across various fields. To facilitate this, we used the integrated e-procurement and R&D notification system and added meta information to aid in the identification and understanding of KOSs, which includes media wiki pages. Furthermore, link information was provided between the BARTOC registry and the Korean KOS websites and media wiki pages.

Capsule hotels are a revolutionary Japanese concept of lodging that dates back over four decades. On the other hand, capsule hotels are a relatively new concept for most travelers outside of Japan. Organizations within this target segment are starting to recognize the critical role that an excellent customer experience management (CEM) strategy offers in improving competitiveness and organizational success. Thus, this research provides scholastic insight into the framework of CEM by evaluating the user-generated content at capsule hotels.

This study inspected 1,304 online user-generated content from the top 10 capsule hotels from Booking.com. Leximancer 4.5 was deployed to analyze the data.

The analyses revealed nine key themes to CEM of capsule hotels which are “staff,” “hotel,” “area,” “location,” “bed,” “capsule,” “check-in,” “noisy” and “luggage”.

This research encourages hospitality and tourism executives to develop specific strategies for capsule hotels.

This research differs from previous writings in that it attempts to fill a gap in the research by offering insight into the issue in the low-budget hotel industry and by identifying key indicators that influence customer experience.

The interpretation of any emerging form or period in art history was never a trivial task. However, in the case of digital art, technology, becoming an integral part, multiplied the complexity of describing, systematizing and evaluating it. This article investigates the most common metadata standards for the documentation of art as a broad category and suggests possible next steps toward an extended metadata standard for digital art.

Describing several techno-cultural phenomena formed in the last decade, manifesting the extendibility of digital art (its ability to be easily extended across multiple modalities), the article, at first, points to the long overdue need to re-evaluate the standards around it. Then it suggests a deeper analysis through a comparative study. In the scope of the study three artworks, The Arnolfini Portrait (Jan van Eyck), an iconic example of the early Renaissance, The World's First Collaborative Sentence (Douglas Davis), a classic example of early Internet art and Fake It Till You Make It (Maya Man), a prominent example of the blockchain art, are examined following the structure of the VRA Core 4.0 standard.

The comparative study demonstrates that digital art is more multi-semantic than traditional physical art, and requires new taxonomies as well as approaches for data acquisition.

Acknowledging that digital art simply has not yet evolved to the stage of being systematically collected by cultural institutions for documentation, curation and preservation, but otherwise, in the past few years, it has been at the front-center of social, economic and technological trends, the article suggests looking for hints on the future-proof extended metadata standard in some of those trends.

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 analyse the recommendations on knowledge organisation from guidelines, policies and procedure manuals of a sample of institutional repositories and networks within the Latin American area and observe the level of follow-up of international guidelines.

Presented is an exploratory and descriptive study of repositories’ professional documents. This study comprised four steps: definition of convenience sample; development of data codebook; coding of data; and analysis of data and conclusions drawing. The convenience sample includes representative sources at three levels: local institutional repositories, national aggregators and international network and aggregators. The codebook gathers information from the repositories’ sample, such as institutional rules and procedure manuals openly available, or recommendations on the use of controlled vocabularies.

The results indicate that at the local repository level, the use of controlled vocabularies is not regulated, leaving the choice of terms to the authors’ discretion. It results in a set of unstructured keywords, not standardised terms, mixing subject terms with other authorities on persons, institutions or places. National aggregators do not regulate these issues either and limit to pointing to international guidelines and policies, which simply recommend the use of controlled vocabularies, using URIs to facilitate interoperability.

The originality of this study lies in identifying how the principles of knowledge organisation are effectively applied by institutional repositories, at local, national and international levels.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.