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Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.

The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.

This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.

This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.

The purpose of the paper is to analyze how the Neo-documentalist movement, initiated in 1996 by Michael Buckland, Boyd Rayward and Niels Lund, has evolved in its 27 years history, how the choice of documentation as name of the new program in Tromsø has made a difference in the LIS field and how different documentation scholars around the world has participated and approached the movement until now.

The paper has approached the “Neo-documentalist movement” in a historical perspective from 1996 to 2023 discussing what difference does the choice of a concept make, when the concept of documentation is chosen instead of information in the name of a program and for the general discussion of the object of an academic field like Library and Information Science.

The analysis shows that it did make a difference to choose the concept of documentation as name of the program in Tromsø and the Neo-documentalist movement contributed to a new focus and discussion of the informative objects, the documents and their creation, not only in Tromsø, but in different parts of the world across linguistic borders.

The paper is original by the fact that it is the first time that the neo documentalist movement has been reviewed on a global scale across linguistic barriers. It has value by a discussion of the ways in which a choice of concept matter in relation to defining a field and the research agenda.

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/).

The purpose of this paper is to provide a new and useful formulation of relevance.

This paper is formulated as a conceptual argument. It makes the case for the utility of considering relevance to be function of use in creative processes.

There are several corollaries to formulating relevance as a function of use. These include the idea that objects by themselves cannot be relevant since use assumes interaction; the affordances of objects and how they are perceived can affect what becomes relevant but are not in themselves relevant; relevance is not an essential characteristic of objects; relevance is transient; potential relevance (what might be relevant in the future) can be distinguished from what is relevant in use and from what has been relevant in the past.

The paper shows that its new formulation of relevance brings improved conceptual and terminological clarity to the discourse about relevance in information science. It demonstrates that how relevance is articulated conceptually is important as its conceptualization can affect the ways that users are able to make use of information systems and, by extension, how information systems can facilitate or disable the co-production of creative outcomes. The paper also usefully expands investigative opportunities by suggesting relevance and creativity are interrelated.

The purpose of the study is to identify the latent topics from 9102 Web of Science (WoS) indexed research articles published in 2645 journals of the Sri Lankan authors from 1989 to 2021 by applying Latent Dirichlet Allocation to the abstracts. Dominant topics in the corpus of text, the posterior probability of different terms in the topics and the publication proportions of the topics were discussed in the article.

Abstracts and other details of the studied articles are collected from WoS database by the authors. Data preprocessing is performed before the analysis. “ldatuning” from the R package is applied after preprocessing of text for deciding subjects in light of factual elements. Twenty topics are decided to extract as latent topics through four metrics methods.

It is observed that medical science, agriculture, research and development and chemistry-related topics dominate the subject categories as a whole. “Irrigation” and “mortality and health care” have a significant growth in the publication proportion from 2019 to 2021. For the most occurring latent topics, it is seen that terms like “activity” and “acid” carry higher posterior probability.

Topic models permit us to rapidly and efficiently address higher perspective inquiries without human mediation and are also helpful in information retrieval and document clustering. The unique feature of this study has highlighted how the growth of the universe of knowledge for a specific country can be studied using the LDA topic model.

This study will create an incentive for text analysis and information retrieval areas of research. The results of this paper gave an understanding of the writing development of the Sri Lankan authors in different subject spaces and over the period. Trends and intensity of publications from the Sri Lankan authors on different latent topics help to trace the interests and mostly practiced areas in different domains.

The purpose of this study was a longitudinal study on knowledge organization (KO) realm structure and cluster concepts and emerging KO events based on co-occurrence analysis.

This longitudinal study uses the co-occurrence analysis. This research population includes keywords of articles indexed in the Web of Science Core Collection 1975–1999 and 2000–2018. Hierarchical clustering, multidimensional scaling and co-occurrence analysis were used to conduct the present research. SPSS, UCINET, VOSviewer and NetDraw were used to analyze and visualize data.

The “Information Technology” in 1975–1999 and the “Information Literacy” in 2000–2018, with the highest frequency, were identified as the most widely used keywords of KO in the world. In the first period, the cluster “Knowledge Management” had the highest centrality, the cluster “Strategic Planning” had the highest density in 2000–2018 and the cluster “Information Retrieval” had the highest centrality and density. The two-dimensional map of KO’s thematic and clustering of KO topics by cluster analysis method indicates that in the periods examined in this study, thematic clusters had much overlap in terms of concept and content.

The present article uses a longitudinal study to examine the KO’s publications in the past half-century. This paper also uses hierarchical clustering and multidimensional scaling methods. Studying the concepts and thematic trends in KO can impact organizing information as the core of libraries, museums and archives. Also, it can scheme information organizing and promote knowledge management. Because the results obtained from this article can help KO policymakers determine and design the roadmap, research planning, and micro and macro budgeting processes.

The article attempts to shed light on the social aspects of research that deal with Sustainable Development Goals (SDGs) and sustainable cities. The aim is to offer a global view of these facets' evolution and to provide information on people-centered smart cities.

The research is qualitative. A systematic bibliometric approach is a framework for the research. The unit of analysis is publications on SDGs and Smart Cities (SCs) indexed in Scopus. The authors used VOSviewer text mining functionality to construct co-occurrence networks of socially related critical terms extracted from textual data. The co-occurrence of keywords presents a valuable method and process for attaining in-depth analysis and fast comprehension of trends and linkages in articles from a holistic approach.

Social media, social sustainability and social capital are the three multifaceted social keywords that co-occur in SDGs and SCs. The paper provides a brief compendium of resources and frameworks to build a socially sustainable smart city.

The retrieval date was on 15 August 2019. The authors used the same search query for new papers released in 2019 and afterwards to update their findings. The authors collected 657 documents on SCs, compared to 2,975 documents about SDGs demonstrating that their findings are still trending in the same direction, emphasizing the importance of the research topic. SCs' social aspects are still chartered areas that require the attention to future research.

The authors’ decision to use two separate data sets for SCs and SDGs data files helps to provide a more comprehensive picture of the research landscape. It may identify areas where research is lacking or needs future research. The authors present an integrative agenda for a smart city to be socially sustainable. Innovative approaches to urban planning are required to empower the place and context and improve the users' satisfaction, where innovative solutions enable smart, sustainable and inclusive societies. Infrastructure governance is a critical keystone. It could guarantee that public investments contribute to sustainable urban development while enhancing city resilience, particularly in facing climate change and inclusive growth challenges.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

The purpose of this study is to conduct a bibliometric analysis to discover the published information on the subject of “Library Automation Software.”

This study is based on bibliometric research to analyze the growth of literature on the subject area “Library Automation Software.” The data were retrieved from the leading database Web of Science to analyze the scholarly publications on the topic during 2001–2022. To analyze the bibliometric data on the subject, the VOSviewer, Biblioshiny and MS Excel software were used.

Results manifest that the topic of “Library Management Systems” has been greatly used by several authors as a title to publish their research work. The USA emerged as the most productive country. The most used keywords to retrieve literature on the topic were “Library Automation,” “Library Management Systems,” “KOHA,” “Integrated Library Systems” and “Open Source Software.”

To the best of the authors’ knowledge, this is the first bibliometric study in Pakistan that has been conducted to measure the productivity of literature at the international level on library automation.

Documentation plays a key role in navigating the costs of construction projects. Traditional document management systems (TDMS) used in developing countries, however, hinder the achievement of expected cost targets. Although the electronic document management system (EDMS) has been implemented to improve documentation, the Sri Lankan construction industry has failed to effectively adapt to it. Hence, this study aims to provide strategies for the effective application of EDMS to the cost management of Sri Lankan mega construction projects.

This study uses a qualitative approach followed by 12 semi-structured expert interviews. Quantity surveying experts were selected through judgemental sampling. Manual content analysis was used to analyse the data.

The EDMS is more suitable for megaprojects than traditional methods of documentation in terms of functionality, neutrality, interoperability, space, reversibility and delivery speed. However, there are contradictory views about cost and security. Furthermore, five transitional challenges of EDMS have been identified under the three key themes of cost, stakeholder perception and technical difficulties. Four reasons were also identified as causing these five challenges. Seven suggestions were made to deal with these transitional challenges and three key feasible solutions for the Sri Lankan construction industry regarding the EDMS were identified. Development of Sri Lankan software with low initial cost was highlighted as the most feasible solution.

This is a novel study to investigate the applicability of EDMS to cost management mechanisms of megaprojects in Sri Lanka. The findings reveal transitional challenges and appropriate feasible solutions for EDMS adaptation. This can be applied to the cost management of megaprojects in other developing countries as well.