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Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials.

This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts.

Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability.

This work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.

This research investigates students’ information-seeking behaviours at the Indian Maritime University (IMU) and assesses the impact of AI chatbots on their marine science knowledge and awareness. The study aims to provide insights into the role of AI-driven solutions in enhancing knowledge sharing and the challenges faced in using AI tools for marine information retrieval.

The study used a stratified random sampling method, encompassing 152 respondents from IMU’s B.Sc. in Nautical Science and B. Tech in Marine Engineering programs. Data collection involved a structured electronic survey questionnaire. The analysis encompassed descriptive statistics using SPSS.

Information needs were met through diverse channels, with 57.9% of respondents using AI-driven chatbots for marine information retrieval. AI significantly recommended research papers (61.8%). The chatbot positively impacted marine science awareness and knowledge, with a mean satisfaction rating of approximately 3.3. Challenges included insufficient access to AI tools, data privacy concerns and accuracy issues.

This study contributes original insights into the information-seeking behaviours of marine students at IMU and the impact of AI chatbots on their knowledge and awareness. It highlights the multifaceted nature of marine information retrieval, the effectiveness of AI-driven solutions in enhancing knowledge sharing and the challenges that need to be addressed for the broader adoption of AI tools in this context.

An increasing number of images are generated daily, and images are gradually becoming a search target. Content-based image retrieval (CBIR) is helpful for users to express their requirements using an image query. Nevertheless, determining whether the retrieval system can provide convenient operation and relevant retrieval results is challenging. A CBIR system based on deep learning features was proposed in this study to effectively search and navigate images in digital articles.

Convolutional neural networks (CNNs) were used as the feature extractors in the author's experiments. Using pretrained parameters, the training time and retrieval time were reduced. Different CNN features were extracted from the constructed image databases consisting of images taken from the National Palace Museum Journals Archive and were compared in the CBIR system.

DenseNet201 achieved the best performance, with a top-10 mAP of 89% and a query time of 0.14 s.

The CBIR homepage displayed image categories showing the content of the database and provided the default query images. After retrieval, the result showed the metadata of the retrieved images and links back to the original pages.

With the interface and retrieval demonstration, a novel image-based reading mode can be established via the CBIR and links to the original images and contextual descriptions.

This paper evaluates the precision of four metasearch engines (MSEs) – DuckDuckGo, Dogpile, Metacrawler and Startpage, to determine which metasearch engine exhibits the highest level of precision and to identify the metasearch engine that is most likely to return the most relevant search results.

The research is divided into two parts: the first phase involves four queries categorized into two segments (4-Q-2-S), while the second phase includes six queries divided into three segments (6-Q-3-S). These queries vary in complexity, falling into three types: simple, phrase and complex. The precision, average precision and the presence of duplicates across all the evaluated metasearch engines are determined.

The study clearly demonstrated that Startpage returned the most relevant results and achieved the highest precision (0.98) among the four MSEs. Conversely, DuckDuckGo exhibited consistent performance across both phases of the study.

The study only evaluated four metasearch engines, which may not be representative of all available metasearch engines. Additionally, a limited number of queries were used, which may not be sufficient to generalize the findings to all types of queries.

The findings of this study can be valuable for accreditation agencies in managing duplicates, improving their search capabilities and obtaining more relevant and precise results. These findings can also assist users in selecting the best metasearch engine based on precision rather than interface.

The study is the first of its kind which evaluates the four metasearch engines. No similar study has been conducted in the past to measure the performance of metasearch engines.

This study aimed to investigate how the enrichment of medical documents' index terms by their comments improves the relevance and novelty of the top-ranked results retrieved by an NLP system.

A semi-experimental pre-test and post-test research was designed to compare NLP-based indexes before and after being expanded by the comment terms. The experiments were conducted on a test collection of 13,957 documents commented by F1000-Prime reviewers. They were indexed at title, abstract, body and full-text levels. In total, 100 seed documents were randomly selected and served as queries. The textual similarity of the documents and queries was calculated using Lucene-more-like-this function and evaluated by the semantic similarity of their MeSH. The results novelty was measured using maximal marginal relevance and evaluated by their MeSH novelties. Normalized discounted cumulative gain was used to compare the basic and expanded indexes' precisions at 10, 20 and 50 top ranks.

The relevance and novelty of the results ranked at the top precision points was improved after expanding the indexes by the comment terms. The finding implies that meta-texts are effective in representing their mother documents, by adding dynamic elements to their rather static contents. It also provides further evidence about the merits of the application of social intelligence and collective wisdom reflected in the actions and reactions of users in tackling the challenges faced by NLP-based systems.

This is the first study to confirm that social comments on scientific papers improve the performance of information systems in terms of relevance and novelty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0283.

The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

Based on an exploratory case-based approach, the purpose of this paper is to open the KM black box and examine the relationships that link knowledge management (KM) inputs (i.e. knowledge resources and KM practices) via knowledge processes to KM performance. This paper aims to identify the underlying mechanisms and explain how KM performance is enabled.

This in-depth case study conducted at a medium-sized consultancy in the supply chain management industry empirically examines knowledge flows to uncover the relationships between KM inputs, knowledge processes and KM performance. We adopt the viable system model (VSM) as a theoretical lens to identify KM mechanisms.

By identifying six KM mechanisms, we contribute to the theoretical understanding of how KM inputs are interconnected and lead to KM performance via knowledge processes.

Based on the insights gained, we provide propositions that organizations should consider in designing viable KM. Our findings help organizations in understanding their KM with the help of knowledge flow analysis and identifying how critical KM elements are interconnected.

The purpose of this paper is to propose a methodology for the enrichment and tailoring of a knowledge organization system (KOS), in order to support the information extraction (IE) task for the analysis of documents in the tourism domain. In particular, the KOS is used to develop a named entity recognition (NER) system.

A method to improve and customize an available thesaurus by leveraging documents related to the tourism in Italy is firstly presented. Then, the obtained thesaurus is used to create an annotated NER corpus, exploiting both distant supervision, deep learning and a light human supervision.

The study shows that a customized KOS can effectively support IE tasks when applied to documents belonging to the same domains and types used for its construction. Moreover, it is very useful to support and ease the annotation task using the proposed methodology, allowing to annotate a corpus with a fraction of the effort required for a manual annotation.

The paper explores an alternative use of a KOS, proposing an innovative NER corpus annotation methodology. Moreover, the KOS and the annotated NER data set will be made publicly available.

Artificial intelligence (AI) is one of today’s rising technologies. AI is a commonly used technology in library services that have the potential to revolutionise the best offerings in the information age. With AI in libraries, users can explore the world of knowledge like never before with smart recommendations tailored to their needs. Overall, AI can enhance the library experience of both the users and library professionals with innovation and smart decisions. Hence, there is no doubt that AI and libraries have a close relationship; nonetheless, the usage and understanding of AI in library services continue to raise concerns, especially in the developing countries which this paper addresses. The purpose of this research paper is to review the current prospects and challenges of implementing AI in library services in developing countries. The primary objective of the study is to discern the pivotal predicaments and obstacles these nations face while implementing AI-based solutions and to propose pragmatic solutions.

The present study adopts a qualitative approach, using content analysis techniques to glean meaningful insights. An extensive review of the extant literature on the subject was conducted, which was meticulously analysed to furnish the findings of this study. The review is limited to English language sources, and searches were conducted using various online academic databases.

The review reveals that the prospects of implementing AI in library services in developing countries are significant, with potential benefits including improved access to information, increased efficiency and productivity and enhanced user experience. However, the review also identifies several challenges, including the lack of infrastructure and resources, the shortage of skilled personnel, the absence of data privacy regulations, digital divide and the high cost of implementing AI-based solutions.

The review suggests several practical solutions to overcome the challenges faced by developing countries in implementing AI in library services. These include partnerships between libraries and technology firms, investment in infrastructure and resources, training and capacity building for library staff and the development of regulatory frameworks to protect user data.

This research paper provides a comprehensive review of the prospects and challenges of implementing AI in library services in developing countries. The study is original in its focus on the perspectives of developing countries, their problems and obstacles. The study also provides practical recommendations that can be used by library managers, policymakers and technology firms to support the implementation of AI-based solutions in developing countries.

This study aims to explore the eye movement behavior of preadolescent children accessing and diagnosing information.

The researchers tracked the eye movements of 30 children with an eye-tracking apparatus. Using the kit of factor-referenced cognitive tests to measure perceptual speed and associative memory, they measured information-searching behavior with screen recordings, the data of which were analyzed by IBM SPSS Statistics 26.

Regarding information accessibility, there was a correlation between the child’s age, associative memory and the number of round-trip choices, and there were differences in the total fixation area among children of different age groups. Regarding diagnosticity, perceptual speed was positively correlated with the total fixation area, and the number of round-trip choices was negatively correlated with fixation duration.

Empirical evidence suggests that during information encoding, perceptual speed is the most important influencing factor. Extensive research indicates that children predominantly rely on recall and familiarity when searching for new information, both of which play roles in associative memory. Through an examination of the psychological and behavioral indicators of children, the study elucidated the cognitive processes involved in information processing and how children engage with information at both visual and cognitive levels.