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The purpose of this study is to review the application of semantic technologies in cultural heritage (STCH) to achieve interoperability and enable advanced applications like 3D modeling and augmented reality by enhancing the understanding and appreciation of CH. The study aims to identify the trends and patterns in using STCH and provide insights for scholars and policymakers on future research directions.

This research paper uses a bibliometric study to analyze the articles published in Scopus and Web of Science (WoS)-indexed journals from 1999 to 2022 on STCH. A total of 580 articles were analyzed using the Biblioshiny package in RStudio.

The study reveals a substantial increase in STCH publications since 2008, with Italy leading in contributions. Key research areas such as ontologies, semantic Web, linked data and digital humanities are extensively explored, highlighting their significance and characteristics within the STCH research domain.

This study only analyzed articles published in Scopus and WoS-indexed journals in the English language. Further research could include articles published in other languages and non-indexed journals.

This study extensively analyses the research published on STCH over the past 23 years, identifying the leading authors, institutions, countries and top research topics. The findings provide guidelines for future research direction and contribute to the literature on promoting, preserving and managing the CH globally.

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.

With the technological innovation dominating higher education, the university libraries, as physical spaces, continue to play a crucial role in connecting students with knowledge. The emergence of the “Learning Commons” serves as a catalyst in the design of library spaces, renewing libraries’ roles and missions and making them popular to Millennials for new reasons. This study aims to record Greek librarians' views on the current situation in Greek academic libraries regarding physical space design, services provided and the existence of the “Learning Commons” model characteristics.

This study was conducted through an online survey structured questionnaire (closed-ended, five-point Likert scale, multiple-choice and statements questions). This study population comprised librarians working in 37 academic institutions and colleges. The collected data were subjected to descriptive statistical analysis. The research questions were answered using variables creation and the tests, t-test, ANOVA and Kruskal–Wallis. The groups of questions were tested for their reliability using the Cronbach's alpha coefficient.

In total, 186 librarians responded to this study. The responses revealed that participants were willing and ready to accept and support a different approach to academic library physical spaces use, according to the shared learning spaces model. However, this requires changes in the organization's vision, further growth and evolution. Although recently most Greek academic libraries have made significant progress in developing their services to function as information and digital hubs, they do not function as learning collaborative hubs, since the “Learning Commons” model is not reflected in their buildings.

The creation of new academic libraries according to the “common” model is a rapidly evolving issue that affects Greek libraries. This paper highlights the characteristics of libraries that should be adopted in the modern era, the new roles of academic librarians and the importance of an appropriate design of the physical space to achieve optimal learning outcomes. At the same time, this paper is one of the few that illustrates librarians', and not users', perceptions of these changes. This paper is a good research example, and the methodology for measuring this type of context could be used by other future research approaches in other countries.

The purpose of this study is to offer a checklist that can be used for both creating and evaluating digital collections, which are also sometimes referred to as data sets as part of the collections as data movement, suitable for computational use.

The checklist was built by synthesising and analysing the results of relevant research literature, articles and studies and the issues and needs obtained in an observational study. The checklist was tested and applied both as a tool for assessing a selection of digital collections made available by galleries, libraries, archives and museums (GLAM) institutions as proof of concept and as a supporting tool for creating collections as data.

Over the past few years, there has been a growing interest in making available digital collections published by GLAM organisations for computational use. Based on previous work, the authors defined a methodology to build a checklist for the publication of Collections as data. The authors’ evaluation showed several examples of applications that can be useful to encourage other institutions to publish their digital collections for computational use.

While some work on making available digital collections suitable for computational use exists, giving particular attention to data quality, planning and experimentation, to the best of the authors’ knowledge, none of the work to date provides an easy-to-follow and robust checklist to publish collection data sets in GLAM institutions. This checklist intends to encourage small- and medium-sized institutions to adopt the collection as data principles in daily workflows following best practices and guidelines.

Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.

In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.

The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.

The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

This study presents a review based research framework that aims to influence memory institutions in their projects on digital storytelling from digitized ancient travel records. This study aims to influence research and policymaking related to design and delivery of services based on memory institutions’ collections of historical records.

The demonstrated research framework has been synthesized using inputs from a review of existing studies on the domain accompanied by a short survey created for collecting the opinion of selected experts. Studies demonstrating utilization of semantic web technologies and those that can influence policymaking related to digital storytelling were primarily reviewed.

The core tasks behind digital storytelling vary depending on the project goals. So, a two-part framework had to be proposed that covers the generic fundamental tasks with diverse applicability and digital storytelling related specific tasks separately. Also during the review, it was found that studies demonstrating the use of travel records for digital storytelling were less in number compared to studies using digital storytelling for tourism in general.

The demonstrated research framework can guide memory institutions in exposing their travel-related holdings to a wider audience using innovative semantic web technologies and open up avenues for future empirical research thereby adding to the novelty of the presented research. Also, reviews of articles on digital storytelling or digital humanities in general exist, but, review of digital storytelling initiatives focusing specifically on tourism and travel literature is scarce.

With the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.

Based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.

The results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.

There is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

This study aims to adjust Thesis-type properties on Schema.org using metadata models and standards (MS) (Bibframe, electronic thesis and dissertations [ETD]-MS, Common European Research Information Format [CERIF] and Dublin Core [DC]) to enrich the Thesis-type properties for better description and processing on the Web.

This study is applied, descriptive analysis in nature and is based on content analysis in terms of method. The research population consisted of elements and attributes of the metadata model and standards (Bibframe, ETD-MS, CERIF and DC) and Thesis-type properties in the Schema.org. The data collection tool was a researcher-made checklist, and the data collection method was structured observation.

The results show that the 65 Thesis-type properties and the two levels of Thing and CreativeWork as its parents on Schema.org that corresponds to the elements and attributes of related models and standards. In addition, 12 properties are special to the Thesis type for better comprehensive description and processing, and 27 properties are added to the CreativeWork type.

Enrichment and expansion of Thesis-type properties on Schema.org is one of the practical applications of the present study, which have enabled more comprehensive description and processing and increased access points and visibility for ETDs in the environment Web and digital libraries.

This study has offered some new Thesis type properties and CreativeWork levels on Schema.org. To the best of the authors’ knowledge, this is the first time this issue is investigated.

The purpose of this study is to determine which metadata schemas are used in the museums and university collections of the main universities in Spain and other European countries. Although libraries and archives are also university memory institutions (according to a Galleries, Libraries, Archives and Museums perspective), their collections are not included in this study because their metadata systems are highly standardized and their inclusion would, therefore, skew our understanding of the diverse realities that the study aims to capture.

The analysis has three components. The first is a bibliographic review based on Web of Science. The second is a direct survey of the individuals responsible for university collections to understand their internal work and documentation systems. Finally, the results obtained are complemented by an analysis of collective university heritage portals in Europe.

The results of this study confirmed the hypothesis that isolation and a lack of resources are still major issues in many cases. Increasing digitalization and the desire to participate in content aggregation systems are forcing change, although the responsibility for that change at universities is still vague.

Universities, particularly those with a long history, have an important heritage whose parts are often scattered or hidden. Although many contemporary academic publications have focused on the dissemination of university collections, this study focuses on the representation of information based on the conviction that good metadata are essential for dissemination.

This study aims to look at challenges that hinder small- and medium-sized enterprises (SMEs) from using open data (OD). The research gaps identified are then used to propose a next generation of OD platform (ODP+).

This study proposes a more effective platform for SMEs called ODP+. A proof of concept was implemented by using modern techniques and technologies, with a pilot conducted among selected SMEs and government employees to test the approach’s viability.

The findings identify current OD platforms generally, and in Gulf Cooperation Council (GCC) countries, they encounter several difficulties, including that the data sets are complex to understand and determine their potential for reuse. The application of big data analytics in mitigating the identified challenges is demonstrated through the artefacts that have been developed.

This paper discusses several challenges that must be addressed to ensure that OD is accessible, helpful and of high quality in the future when planning and implementing OD initiatives.

The proposed ODP+ integrates social network data, SME data sets and government databases. It will give SMEs a platform for combining data from government agencies, third parties and social networks to carry out complex analytical scenarios or build the needed application using artificial intelligence.

The findings promote the potential future utilisation of OD and suggest ways to give users access to knowledge and features.

To the best of the authors’ knowledge, no study provides extensive research about OD in Qatar or GCC. Further, the proposed ODP+ is a new platform that allows SMEs to run natural language data analytics queries.