The Electronic Library最新文献

{"title":"Readiness of Pakistani journals for open access publishing","authors":"Muhammad Zahid Raza, Muhammad Rafiq, S. Soroya","doi":"10.1108/el-11-2023-0279","DOIUrl":"https://doi.org/10.1108/el-11-2023-0279","url":null,"abstract":"\u0000Purpose\u0000This study was designed to discover the readiness of the higher education commission (HEC)-recognized journals of Pakistan in terms of human, financial and technological resources, technical expertise, institutional support, availability of open access (OA) policy, availability of guidance and training, willingness, motivation and so on for OA journal publishing and to expose the challenges in OA journal publishing.\u0000\u0000\u0000Design/methodology/approach\u0000A quantitative research approach was used and a structured questionnaire was developed to meet the objectives of this study. A survey method was used to collect data from the editors of all 329 HEC-recognized journals in Pakistan.\u0000\u0000\u0000Findings\u0000The respondents of all the HEC-recognized journals of Pakistan are neutral and are not of the view that they have sufficient financial, human, technological/infrastructural resources and technical expertise to continue/initiate an OA journal publishing. ‘No academic reward’; and ‘no monetary reward for the editorial staff’ are both enormous challenges for OA journal publishing. The perceived challenges of OA have a negative impact on readiness for OA publishing. The readiness level of the respondents of the OA journals is higher as compared to the readiness level of the respondents of non-OA journals.\u0000\u0000\u0000Research limitations/implications\u0000This study covered the lists of HEC-recognized journals of 2019. More studies may be conducted based on updated lists of HEC-recognized journals. Qualitative studies may also be conducted to discover the readiness of the HEC-recognized journals of Pakistan for OA journal publishing.\u0000\u0000\u0000Originality/value\u0000This study is the first comprehensive study on this phenomenon and is an effort to fill this gap to invigorate scholarly literature. It may attract the attention of policymakers, funding bodies, parent institutions of the journals and the HEC regarding the readiness of journals in terms of financial, human, technological/infrastructural resources, technical expertise of the journals and challenges of journals to prompt the OA journal publishing paradigm.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"109 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the construction and application of a smart library services maturity evaluation system based on CMM","authors":"Zhongxian Bai, Lvna Yu, Lei Zhao, Weijia Wang","doi":"10.1108/el-11-2023-0284","DOIUrl":"https://doi.org/10.1108/el-11-2023-0284","url":null,"abstract":"\u0000Purpose\u0000Smart libraries are the result of the application of smart technologies in the era of digital intelligence. The establishment and improvement of its service evaluation system serve as indicators for evaluating the growth of smart libraries.\u0000\u0000\u0000Design/methodology/approach\u0000This study introduces and improves the capability maturity model (CMM), creatively constructs a service maturity model specifically designed for smart libraries and combines the Delphi method with the analytic hierarchy process (AHP) to establish a service maturity evaluation system for smart libraries while calculating indicator weights. Finally, two representative smart libraries are selected as case studies, and an empirical application is conducted using the fuzzy comprehensive evaluation method.\u0000\u0000\u0000Findings\u0000The empirical study shows that the developed smart libraries service maturity evaluation system holds significant theoretical and practical value in evaluating smart libraries.\u0000\u0000\u0000Originality/value\u0000Enhances the CMM and creatively constructs a service maturity model for smart libraries. Combines the Delphi method with AHP to establish a service maturity evaluation system while calculating indicator weights. Uses a fuzzy comprehensive evaluation method to evaluate two representative smart libraries. Demonstrates that the smart library services maturity evaluation system holds significant theoretical and practical value.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"42 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method of identifying domain-specific academic user information needs based on academic Q&A communities","authors":"Chunxiu Qin, Yulong Wang, Xubu Ma, Yaxi Liu, Jin Zhang","doi":"10.1108/el-12-2023-0310","DOIUrl":"https://doi.org/10.1108/el-12-2023-0310","url":null,"abstract":"\u0000Purpose\u0000To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.\u0000\u0000\u0000Design/methodology/approach\u0000This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.\u0000\u0000\u0000Findings\u0000Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.\u0000\u0000\u0000Originality/value\u0000This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"95 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities","authors":"Ziling Chen, Chengzhi Zhang, Heng Zhang, Yi Zhao, Chengpeng Yang, Yang Yang","doi":"10.1108/el-03-2024-0070","DOIUrl":"https://doi.org/10.1108/el-03-2024-0070","url":null,"abstract":"Purpose\u0000The composition of author teams is a significant factor affecting the novelty of academic papers. Existing research lacks studies focusing on institutional types and measures of novelty remained at a general level, making it difficult to analyse the types of novelty in papers and to provide a detailed explanation of novelty. This study aims to take the field of natural language processing (NLP) as an example to analyse the relationship between team institutional composition and the fine-grained novelty of academic papers.\u0000\u0000Design/methodology/approach\u0000Firstly, author teams are categorized into three types: academic institutions, industrial institutions and mixed academic and industrial institutions. Next, the authors extract four types of entities from the full paper: methods, data sets, tools and metric. The novelty of papers is evaluated using entity combination measurement methods. Additionally, pairwise combinations of different types of fine-grained entities are analysed to assess their contributions to novel papers.\u0000\u0000Findings\u0000The results of the study found that in the field of NLP, for industrial institutions, collaboration with academic institutions has a higher probability of producing novel papers. From the contribution rate of different types of fine-grained knowledge entities, the mixed academic and industrial institutions pay more attention to the novelty of the combination of method indicators, and the industrial institutions pay more attention to the novelty of the combination of method tools.\u0000\u0000Originality/value\u0000This paper explores the relationship between the team institutional composition and the novelty of academic papers and reveals the importance of cooperation between industry and academia through fine-grained novelty measurement, which provides key guidance for improving the quality of papers and promoting industry–university–research cooperation.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"77 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141664735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors influencing the adoption of big data in libraries: a systematic literature review of peer-reviewed articles from 2013 to 2023","authors":"Khurram Shahzad, Shakeel Ahmad Khan","doi":"10.1108/el-02-2024-0057","DOIUrl":"https://doi.org/10.1108/el-02-2024-0057","url":null,"abstract":"Purpose\u0000The purpose of this study are to identify the factors influencing the adoption of big data in libraries, determine the challenges causing the hindrance of big data implementation and reveal the best practices for the efficient adoption of big data in libraries.\u0000\u0000Design/methodology/approach\u0000A systematic literature review was applied to address the objectives of the study. Twenty-two studies published in peer-reviewed journals were selected to conduct the study.\u0000\u0000Findings\u0000The findings of the study revealed that decision-making, service enhancement, professional development and preservation factors influenced the adoption of big data technologies in libraries. The study also displayed that challenges of infrastructure, technical skills, data management and legal considerations caused barriers to the adoption of big data in libraries. Results also revealed that training and professional development, guidelines and policies establishment, leadership and strategic planning and resource allocation proved fruitful in the efficient adoption of big data applications in libraries.\u0000\u0000Originality/value\u0000The study offers theoretical implications for future investigators through the provision of innovative literature on the factors, challenges and best practices associated with big data in the context of librarianship. The study has also provided practical implications for management bodies by offering guidelines for the successful adoption of big data in libraries.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141334729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of knowledge graphs in chatbots","authors":"Enayat Rajabi, Allu Niya George, Karishma Kumar","doi":"10.1108/el-03-2023-0066","DOIUrl":"https://doi.org/10.1108/el-03-2023-0066","url":null,"abstract":"\u0000Purpose\u0000This study aims to investigate the applications of knowledge graphs in developing artificial intelligence (AI) assistants and chatbots by reviewing scholarly publications from different lenses and dimensions. The authors also analyze the various AI approaches used for knowledge graph-driven chatbots and discuss how implementing these techniques makes a difference in technology.\u0000\u0000\u0000Design/methodology/approach\u0000Over recent years, chatbots have emerged as a transformational force in interacting with the digital world in various domains, including customer service and personal assistants. Recently, chatbots have become a revolutionary tool for interacting with the digital world in various contexts, such as personal assistants and customer support. Incorporating knowledge graphs considerably improved the capabilities of chatbots by allowing them access to massive knowledge bases and enhancing their ability to understand queries. Furthermore, knowledge graphs enable chatbots to understand semantic links between elements and improve response quality. This study highlights the role of knowledge graphs in chatbots following a systematic review approach. They have been integrated into major health-care, education and business domains. Beyond improving information retrieval, knowledge graphs enhance the user experience and increase the range of fields in which chatbots can be used. Improving and enriching chatbot answers was also identified as one of the main advantages of knowledge graphs. This enriched response can increase user confidence and improve the accuracy of chatbot interactions, making them more trustworthy information sources.\u0000\u0000\u0000Findings\u0000Knowledge graph-based chatbots leverage extensive data retrieval to provide accurate and enriched responses, increasing user confidence and experience without requiring extensive training. The three major domains where knowledge graph-based chatbots have been used are health care, education and business.\u0000\u0000\u0000Practical implications\u0000Knowledge graph-based chatbots can better comprehend user queries and respond with relevant information efficiently without extensive training. Furthermore, knowledge graphs enable chatbots to understand semantic links between elements, allowing them to answer complicated and multi-faceted questions. This semantic comprehension improves response quality, making chatbots more successful in providing accurate and valuable information in various domains. Furthermore, knowledge graphs enable chatbots to provide consumers with individualized experiences by storing and recalling individual preferences, history or previous encounters. This study analyzes the role of knowledge graphs in chatbots following a systematic review approach. This study reviewed state-of-the-art articles to understand where and how chatbots have used knowledge graphs. The authors found health care, business and education as three main areas in which knowledge-graph-based chatbots have been mostly used. Chatbots ","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"10 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141334908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-feature fusion stock prediction based on knowledge graph","authors":"Zhenghao Liu, Yuxing Qian, Wenlong Lv, Yanbin Fang, Shenglan Liu","doi":"10.1108/el-02-2023-0053","DOIUrl":"https://doi.org/10.1108/el-02-2023-0053","url":null,"abstract":"Purpose\u0000Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.\u0000\u0000Design/methodology/approach\u0000This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.\u0000\u0000Findings\u0000Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.\u0000\u0000Originality/value\u0000The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"10 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141335275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An approach based on open research knowledge graph for knowledge acquisition from scientific papers","authors":"Azanzi Jiomekong, Sanju Tiwari","doi":"10.1108/el-06-2023-0154","DOIUrl":"https://doi.org/10.1108/el-06-2023-0154","url":null,"abstract":"\u0000Purpose\u0000This paper aims to curate open research knowledge graph (ORKG) with papers related to ontology learning and define an approach using ORKG as a computer-assisted tool to organize key-insights extracted from research papers.\u0000\u0000\u0000Design/methodology/approach\u0000Action research was used to explore, test and evaluate the use of the Open Research Knowledge Graph as a computer assistant tool for knowledge acquisition from scientific papers.\u0000\u0000\u0000Findings\u0000To extract, structure and describe research contributions, the granularity of information should be decided; to facilitate the comparison of scientific papers, one should design a common template that will be used to describe the state of the art of a domain.\u0000\u0000\u0000Originality/value\u0000This approach is currently used to document “food information engineering,” “tabular data to knowledge graph matching” and “question answering” research problems and the “neurosymbolic AI” domain. More than 200 papers are ingested in ORKG. From these papers, more than 800 contributions are documented and these contributions are used to build over 100 comparison tables. At the end of this work, we found that ORKG is a valuable tool that can reduce the working curve of state-of-the-art research.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"3 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141265448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis","authors":"Yiming Li, Xukan Xu, Muhammad Riaz, Yifan Su","doi":"10.1108/el-09-2023-0208","DOIUrl":"https://doi.org/10.1108/el-09-2023-0208","url":null,"abstract":"\u0000Purpose\u0000This study aims to use geographical information on social media for public opinion risk identification during a crisis.\u0000\u0000\u0000Design/methodology/approach\u0000This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.\u0000\u0000\u0000Findings\u0000In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.\u0000\u0000\u0000Originality/value\u0000Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"50 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment time series clustering of Danmu videos based on BERT fine-tuning and SBD-K-shape","authors":"Ruoxi Zhang, Chenhan Ren","doi":"10.1108/el-10-2023-0243","DOIUrl":"https://doi.org/10.1108/el-10-2023-0243","url":null,"abstract":"\u0000Purpose\u0000This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.\u0000\u0000\u0000Design/methodology/approach\u0000This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.\u0000\u0000\u0000Findings\u0000The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.\u0000\u0000\u0000Originality/value\u0000Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.\u0000","PeriodicalId":360625,"journal":{"name":"The Electronic Library","volume":"13 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}