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Open Access
Article
Publication date: 20 February 2024

Alenka Kavčič Čolić and Andreja Hari

The current predominant delivery format resulting from digitization is PDF, which is not appropriate for the blind, partially sighted and people who read on mobile devices. To…

Abstract

Purpose

The current predominant delivery format resulting from digitization is PDF, which is not appropriate for the blind, partially sighted and people who read on mobile devices. To meet the needs of both communities, as well as broader ones, alternative file formats are required. With the findings of the eBooks-On-Demand-Network Opening Publications for European Netizens project research, this study aims to improve access to digitized content for these communities.

Design/methodology/approach

In 2022, the authors conducted research on the digitization experiences of 13 EODOPEN partners at their organizations. The authors distributed the same sample of scans in English with different characteristics, and in accordance with Web content accessibility guidelines, the authors created 24 criteria to analyze their digitization workflows, output formats and optical character recognition (OCR) quality.

Findings

In this contribution, the authors present the results of a trial implementation among EODOPEN partners regarding their digitization workflows, used delivery file formats and the resulting quality of OCR results, depending on the type of digitization output file format. It was shown that partners using the OCR tool ABBYY FineReader Professional and producing scanning outputs in tagged PDF and PDF/UA formats achieved better results according to set criteria.

Research limitations/implications

The trial implementations were limited to 13 project partners’ organizations only.

Originality/value

This research paper can be a valuable contribution to the field of massive digitization practices, particularly in terms of improving the accessibility of the output delivery file formats.

Details

Digital Library Perspectives, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5816

Keywords

Article
Publication date: 2 August 2022

Zhongbao Liu and Wenjuan Zhao

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective…

Abstract

Purpose

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective. A specific part of academic literature, such as sentences, paragraphs and chapter contents are also called a level of academic literature in this paper. There are a few comparative research works on the relationship between models, disciplines and levels in the process of structure function recognition. In view of this, comparative research on structure function recognition based on deep learning has been conducted in this paper.

Design/methodology/approach

An experimental corpus, including the academic literature of traditional Chinese medicine, library and information science, computer science, environmental science and phytology, was constructed. Meanwhile, deep learning models such as convolutional neural networks (CNN), long and short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) were used. The comparative experiments of structure function recognition were conducted with the help of the deep learning models from the multilevel perspective.

Findings

The experimental results showed that (1) the BERT model performed best, with F1 values of 78.02, 89.41 and 94.88%, respectively at the level of sentence, paragraph and chapter content. (2) The deep learning models performed better on the academic literature of traditional Chinese medicine than on other disciplines in most cases, e.g. F1 values of CNN, LSTM and BERT, respectively arrived at 71.14, 69.96 and 78.02% at the level of sentence. (3) The deep learning models performed better at the level of chapter content than other levels, the maximum F1 values of CNN, LSTM and BERT at 91.92, 74.90 and 94.88%, respectively. Furthermore, the confusion matrix of recognition results on the academic literature was introduced to find out the reason for misrecognition.

Originality/value

This paper may inspire other research on structure function recognition, and provide a valuable reference for the analysis of influencing factors.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 14 November 2023

Shaodan Sun, Jun Deng and Xugong Qin

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained…

Abstract

Purpose

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

Design/methodology/approach

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

Findings

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Originality/value

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 25 January 2024

Yaolin Zhou, Zhaoyang Zhang, Xiaoyu Wang, Quanzheng Sheng and Rongying Zhao

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned…

Abstract

Purpose

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Design/methodology/approach

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

Findings

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

Originality/value

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Open Access
Article
Publication date: 20 June 2023

William R. Illsley

By reconsidering the concept of the historic environment, the aim of this study is to better understand how heritage is expressed by examining the networks within which the…

Abstract

Purpose

By reconsidering the concept of the historic environment, the aim of this study is to better understand how heritage is expressed by examining the networks within which the cultural performances of the historic environment take place. The goal is to move beyond a purely material expression and seek the expansion of the cultural dimension of the historic environment.

Design/methodology/approach

Conceptually, the historic environment is considered a valuable resource for heritage expression and exploration. The databases and records that house historic environment data are venerated and frequented entities for archeologists, but arguably less so for non-specialist users. In inventorying the historic environment, databases fulfill a major role in the planning process and asset management that is often considered to be more than just perfunctory. This paper approaches historic environment records (HERs) from an actor network perspective, particularizing the social foundation and relationships within the networks governing the historic environment and the environment's associated records.

Findings

The paper concludes that the performance of HERs from an actor-network perspective is a hegemonic process that is biased toward the supply and input to and from professional users. Furthermore, the paper provides a schematic for how many of the flaws in heritage transmission have come about.

Originality/value

The relevance here is largely belied by the fact that HERs as both public digital resources and as heritage networks were awaiting to be addressed in depth from a theoretical point of view.

Details

Journal of Cultural Heritage Management and Sustainable Development, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-1266

Keywords

Article
Publication date: 6 October 2022

Ahmed Gouda Mohamed and Amr Mousa

Current research efforts exhibit a surge imperative for a building information modelling (BIM) approach that embodies a repository of all relevant data of existing building…

Abstract

Purpose

Current research efforts exhibit a surge imperative for a building information modelling (BIM) approach that embodies a repository of all relevant data of existing building components while monitoring and consistently recording numerous components’ functions throughout its lifecycle, especially in Egypt. This research paper aims to develop an integrated as-is BIM-facility management (FM) information model for the existing building’s components via a case study, depicting a repository for historical data and knowledge amassed from inspections and conveying maintenance decisions automatically during the FM practices.

Design/methodology/approach

The developed approach pursues four successive steps: data acquisition and processing of building components; components recognition from point clouds; modelling scanned point clouds; and quick response code information transfer to BIM components.

Findings

The proposed approach incorporates the as-is BIM with the building components’ as-is FM information to portray a repository for historical data and knowledge collected from inspections to proactively benefit facility managers in simplifying, expediting and enhancing maintenance decisions automatically during FM practices.

Originality/value

This paper presents a digital alternative to manual maintenance recordkeeping concerning building components to retrieve their as-is and historical data using a case study in Egypt. This paper proposes a broad scan to as-is information BIM approach for the existing building’s components to condone maintenance interventions using a versatile, affordable, readily available and multi-functional method for scanning the building’s components using a handheld tool.

Details

Journal of Facilities Management , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1472-5967

Keywords

Article
Publication date: 16 February 2022

Pragati Agarwal, Sanjeev Swami and Sunita Kumari Malhotra

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as…

3404

Abstract

Purpose

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

Design/methodology/approach

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

Findings

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research limitations/implications

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

Practical implications

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

Originality/value

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

Details

Journal of Science and Technology Policy Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2053-4620

Keywords

Article
Publication date: 27 February 2023

Dilawar Ali, Kenzo Milleville, Steven Verstockt, Nico Van de Weghe, Sally Chambers and Julie M. Birkholz

Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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/).

Article
Publication date: 5 July 2023

Philip Seagraves

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from…

856

Abstract

Purpose

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from property recommendations to compliance automation, this study highlights potential benefits such as increased accuracy and efficiency. At the same time, this study critically discusses potential drawbacks, like privacy concerns and job displacement. The paper's goal is to offer valuable insights to industry professionals and policy makers, aiding strategic decision-making as AI continues to reshape the landscape of the real estate sector.

Design/methodology/approach

This paper employs an extensive literature review, combined with a qualitative analysis of case studies. Various AI applications in the real estate industry are examined, including machine learning for property recommendations and valuation, VR/AR property tours, AI automation for contract and regulatory compliance, and chatbots for customer service. The study also delves into the optimisation potential of AI in building management, lead generation, and risk assessment, whilst critically discussing potential challenges such as data privacy, algorithmic bias, and job displacement. The outcomes aim to inform strategic decisions for industry professionals and policy makers.

Findings

The study finds that AI has significant potential to revolutionise the real estate industry through enhanced accuracy in property valuation, efficient automation and immersive AR/VR experiences. AI-driven chatbots and optimisation in building management also hold promise. However, this study also uncovers potential challenges, including data privacy issues, algorithmic biases, and possible job displacement due to increased automation. The insights gleaned from this study underscore the importance of strategic decision-making in harnessing the benefits of AI while mitigating potential drawbacks in the real estate sector.

Practical implications

The paper's practical implications extend to industry professionals, policy makers, and technology developers. Professionals gain insights into how AI can enhance efficiency and accuracy in the real estate sector, guiding strategic decision-making. For policy makers, understanding potential challenges like data privacy and job displacement informs regulatory measures. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Additionally, highlighting the need for addressing algorithmic bias and privacy concerns in AI systems may foster better design practices. Therefore, the paper's findings could significantly shape the future trajectory of AI integration in real estate.

Originality/value

The paper provides original value by offering a comprehensive analysis of the transformative impact of AI in the real estate industry. Its multi-faceted examination of AI applications, coupled with a critical discussion on potential challenges, provides a balanced perspective. The paper's focus on informing strategic decisions for professionals and policy makers makes it a valuable resource. Moreover, by considering both benefits and drawbacks, this study contributes to the discourse on AI's broader societal implications. In the context of rapid technological change, such comprehensive studies are rare, adding to the paper's originality.

Details

Journal of Property Investment & Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-578X

Keywords

Article
Publication date: 9 May 2023

Dan Wang

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and…

Abstract

Purpose

This research conducts bibliometric analyses and network mapping on smart libraries worldwide. It examines publication profiles, identifies the most cited publications and preferred sources and considers the cooperation of the authors, organizations and countries worldwide. The research also highlights keyword trends and clusters and finds new developments and emerging trends from the co-cited references network.

Design/methodology/approach

A total of 264 records with 1,200 citations were extracted from the Web of Science database from 2003 to 2021. The trends in the smart library were analyzed and visualized using BibExcel, VOSviewer, Biblioshiny and CiteSpace.

Findings

The People’s Republic of China had the most publications (119), the most citations (374), the highest H-index (12) and the highest total link strength (TLS = 25). Wuhan University had the highest H-index (6). Chiu, Dickson K. W. (H-index = 4, TLS = 22) and Lo, Patrick (H-index = 4, TLS = 21) from the University of Hong Kong had the highest H-indices and were the most cooperative authors. Library Hi Tech was the most preferred journal. “Mobile library” was the most frequently used keyword. “Mobile context” was the largest cluster on the research front.

Research limitations/implications

This study helps librarians, scientists and funders understand smart library trends.

Originality/value

There are several studies and solid background research on smart libraries. However, to the best of the author’s knowledge, this study is the first to conduct bibliometric analyses and network mapping on smart libraries around the globe.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

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