Aslib J. Inf. Manag.最新文献

{"title":"Practices for enhancing research visibility, citations and impact: review of literature","authors":"Sabitri Majhi, Lili Sahu, Kabita Behera","doi":"10.1108/ajim-11-2023-532","DOIUrl":"https://doi.org/10.1108/ajim-11-2023-532","url":null,"abstract":"","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"16 1","pages":"1280-1305"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211826","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":"Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements","authors":"J. Zeng, Shujin Cao, Yijin Chen, Pei Pan, Yafang Cai","doi":"10.1108/ajim-03-2022-0130","DOIUrl":"https://doi.org/10.1108/ajim-03-2022-0130","url":null,"abstract":"PurposeThis study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model.Design/methodology/approachEight research themes were selected for experiment, with a large-scale (N = 11,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements.FindingsThe empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions.Originality/valueThe extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130755141","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 deep active learning-based and crowdsourcing-assisted solution for named entity recognition in Chinese historical corpora","authors":"Chengxi Yan, Xuemei Tang, Haoxia Yang, Jun Wang","doi":"10.1108/ajim-03-2022-0107","DOIUrl":"https://doi.org/10.1108/ajim-03-2022-0107","url":null,"abstract":"PurposeThe majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction.Design/methodology/approachThe research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation (“SA-ALC”), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system.FindingsExtensive experiments on two types of Chinese classical historical datasets show that the authors’ solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors’ pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage.Originality/valueThe study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways.","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116592113","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":"Discovery of topic evolution path and semantic relationship based on patent entity representation","authors":"Jinzhu Zhang, Yue Liu, Linqi Jiang, Jialu Shi","doi":"10.1108/ajim-03-2022-0124","DOIUrl":"https://doi.org/10.1108/ajim-03-2022-0124","url":null,"abstract":"PurposeThis paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.Design/methodology/approachFirstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.FindingsIn the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.Originality/valueThis work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124303752","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":"Detecting the research structure and topic trends of social media using static and dynamic probabilistic topic models","authors":"Muhammad Inaam ul haq, Qianmu Li, J. Hou, Adnan Iftekhar","doi":"10.1108/ajim-02-2022-0091","DOIUrl":"https://doi.org/10.1108/ajim-02-2022-0091","url":null,"abstract":"PurposeA huge volume of published research articles is available on social media which evolves because of the rapid scientific advances and this paper aims to investigate the research structure of social media.Design/methodology/approachThis study employs an integrated topic modeling and text mining-based approach on 30381 Scopus index titles, abstracts, and keywords published between 2006 and 2021. It combines analytical analysis of top-cited reviews with topic modeling as means of semantic validation. The output sequences of the dynamic model are further analyzed using the statistical techniques that facilitate the extraction of topic clusters, communities, and potential inter-topic research directions.FindingsThis paper brings into vision the research structure of social media in terms of topics, temporal topic evolutions, topic trends, emerging, fading, and consistent topics of this domain. It also traces various shifts in topic themes. The hot research topics are the application of the machine or deep learning towards social media in general, alcohol consumption in different regions and its impact, Social engagement and media platforms. Moreover, the consistent topics in both models include food management in disaster, health study of diverse age groups, and emerging topics include drug violence, analysis of social media news for misinformation, and problems of Internet addiction.Originality/valueThis study extends the existing topic modeling-based studies that analyze the social media literature from a specific disciplinary viewpoint. It focuses on semantic validations of topic-modeling output and correlations among the topics and also provides a two-stage cluster analysis of the topics.","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125383522","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":"Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations","authors":"Zheng Wang, Shuo Xu, Yibo Wang, Xiaojiao Chai, Liang Chen","doi":"10.1108/ajim-01-2022-0046","DOIUrl":"https://doi.org/10.1108/ajim-01-2022-0046","url":null,"abstract":"PurposeThe purpose of this study is to solve the problems caused by the growing volumes of pre-annotated literature and variety-oriented annotations, including teamwork, quality control and time effort.Design/methodology/approach An annotation collaboration workbench is developed, which is named as Bureau for Rapid Annotation Tool (Brat). Main functionalities include an enhanced semantic constraint system, Vim-like shortcut keys, an annotation filter and a graph-visualizing annotation browser. With these functionalities, the annotators are encouraged to question their initial mindset, inspect conflicts and gain agreement from their peers.FindingsThe collaborative patterns can indeed be leveraged to structure properly every annotator’s behaviors. The Brat workbench can actually be seen as an experienced-based annotation tool by harnessing collective intelligence. Compared to previous counterparts, about one-third of time can be saved on Xinhuanet military news and patent corpora with the workbench.Originality/valueThe various annotations are very popular in real-world annotation tasks with multiple annotators. Though, it is still under-discussed on variety-oriented annotations. The findings of this study provide the practitioners valuable insight into how to govern annotation projects. In addition, the Brat workbench takes the first step for future research on annotating large-scale text resources.","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122672456","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":"Guest editorial: Human-computer interactions: investigating the dark side and proposing a model based on an empirical collection of studies","authors":"Abhishek Behl, Manish Gupta, V. Pereira, Z. Zhang","doi":"10.1108/ajim-09-2022-398","DOIUrl":"https://doi.org/10.1108/ajim-09-2022-398","url":null,"abstract":"","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"420 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132588582","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":"Which structure of academic articles do referees pay more attention to?: perspective of peer review and full-text of academic articles","authors":"Chenglei Qin, Chengzhi Zhang","doi":"10.1108/AJIM-05-2022-0244","DOIUrl":"https://doi.org/10.1108/AJIM-05-2022-0244","url":null,"abstract":"PurposeThe purpose of this paper is to explore which structures of academic articles referees would pay more attention to, what specific content referees focus on, and whether the distribution of PRC is related to the citations.Design/methodology/approachFirstly, utilizing the feature words of section title and hierarchical attention network model (HAN) to identify the academic article structures. Secondly, analyzing the distribution of PRC in different structures according to the position information extracted by rules in PRC. Thirdly, analyzing the distribution of feature words of PRC extracted by the Chi-square test and TF-IDF in different structures. Finally, four correlation analysis methods are used to analyze whether the distribution of PRC in different structures is correlated to the citations.FindingsThe count of PRC distributed in Materials and Methods and Results section is significantly more than that in the structure of Introduction and Discussion, indicating that referees pay more attention to the Material and Methods and Results. The distribution of feature words of PRC in different structures is obviously different, which can reflect the content of referees' concern. There is no correlation between the distribution of PRC in different structures and the citations.Research limitations/implicationsDue to the differences in the way referees write peer review reports, the rules used to extract position information cannot cover all PRC.Originality/valueThe paper finds a pattern in the distribution of PRC in different academic article structures proving the long-term empirical understanding. It also provides insight into academic article writing: researchers should ensure the scientificity of methods and the reliability of results when writing academic article to obtain a high degree of recognition from referees.","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"31 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123394919","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":"Reviewing topics of COVID-19 news articles: case study of CNN and China daily","authors":"Yue Yuan, Kan Liu, Yanli Wang","doi":"10.1108/ajim-05-2022-0264","DOIUrl":"https://doi.org/10.1108/ajim-05-2022-0264","url":null,"abstract":"PurposeThe purpose of this study is to analyze the topics of COVID-19 news articles for better obtaining the relationship among and the evolution of news topics, helping to manage the infodemic from a quantified perspective.Design/methodology/approachTo analyze COVID-19 news articles explicitly, this paper proposes a prism architecture. Based on epidemic-related news on China Daily and CNN, this paper identifies the topics of the two news agencies, elucidates the relationship between and amongst these topics, tracks topic changes as the epidemic progresses and presents the results visually and compellingly.FindingsThe analysis results show that CNN has a more concentrated distribution of topics than China Daily, with the former focusing on government-related information, and the latter on medical. Besides, the pandemic has had a big impact on CNN and China Daily's reporting preference. The evolution analysis of news topics indicates that the dynamic changes of topics have a strong relationship with the pandemic process.Originality/valueThis paper offers novel perspectives to review the topics of COVID-19 news articles and provide new understandings of news articles during the initial outbreak. The analysis results expand the scope of infodemic-related studies.","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126001312","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":"Band gap information extraction from materials science literature - a pilot study","authors":"Satanu Ghosh, Kun Lu","doi":"10.1108/ajim-03-2022-0141","DOIUrl":"https://doi.org/10.1108/ajim-03-2022-0141","url":null,"abstract":"PurposeThe purpose of this paper is to present a preliminary work on extracting band gap information of materials from academic papers. With increasing demand for renewable energy, band gap information will help material scientists design and implement novel photovoltaic (PV) cells.Design/methodology/approachThe authors collected 1.44 million titles and abstracts of scholarly articles related to materials science, and then filtered the collection to 11,939 articles that potentially contain relevant information about materials and their band gap values. ChemDataExtractor was extended to extract information about PV materials and their band gap information. Evaluation was performed on randomly sampled information records of 415 papers.FindingsThe findings of this study show that the current system is able to correctly extract information for 51.32% articles, with partially correct extraction for 36.62% articles and incorrect for 12.04%. The authors have also identified the errors belonging to three main categories pertaining to chemical entity identification, band gap information and interdependency resolution. Future work will focus on addressing these errors to improve the performance of the system.Originality/valueThe authors did not find any literature to date on band gap information extraction from academic text using automated methods. This work is unique and original. Band gap information is of importance to materials scientists in applications such as solar cells, light emitting diodes and laser diodes.","PeriodicalId":421104,"journal":{"name":"Aslib J. Inf. Manag.","volume":"12 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113967579","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}