Data and information management最新文献

Most of the current research on the information analysis of social media (SM) for public emergency focused on a single dimension such as emotion while neglecting the interaction between multidimensional information. Therefore, in this study, an information dispersing–superimposing model is proposed to explain the implicit regularity of the impact within a symbol, sentiment, and context information and their dependent evolution on the SM. Information hue, saturation, and flux (HSF) are defined to measure the interaction process. An online event was selected to verify the concept and hypothesis of this study. The results proved that the interaction among multidimensional information did exist on the SM for a public emergency. The turning points of information dispersing–superimposing often emerged when the number of online users involved had significant changes, and sentiment and context information were showed to have a strong interaction relationship and tended to be spread at the same time. It was also manifested that the dominant information component was varied at each stage of the emergency. This paper is one of the first to study the interaction of multidimensional information on the SM derived from optics scattering. The findings of the study will try to provide a theoretical explanation for why certain information components may be enhanced during the online dissemination and suggest practical support for the information predictions and interface design for SM.

With the increasing pressure on the National Institutes of Health (NIH) budget nowadays, it is such a major challenge to cut waste and improve efficiency in the research funding allocation. To meet this challenge, this paper explores research hotspots and disciplinary trends of the biomedical area, and discusses the relationship between these factors and the government funding, thereby uncovering biomedical hotspots of interest to academia and the evolution law of the U.S. federal government funding through an entitymetrics analysis. Considering that the rapid proliferation of biomedical literature provides large amounts of information resources for knowledge discovery, entities extracted from articles in PubMed and NIH-funded projects during 1988–2017 are taken as experimental data. They are divided into four categories: species, diseases, genes, and drugs. Subsequently, a comparative analysis of entity trajectories in the four domains is performed, which includes occurrence frequency calculations of disease entities to explore frequency variation trends in high-frequency entities and the situation of the distribution of research funds. Finally, we conduct an evolutionary analysis of two sides, respectively: the relationship between research popularity and the amount of funding; the relationship between research popularity and the number of funded projects. The results suggest that research on gene and disease entities is at the stage of rapid development. Diseases with high prevalence rate and mortality and diseases associated with genetic factors will be the emphasis of research trends in the future. The distribution of NIH grant appears obvious long tail effect and can influence overall trends in the heat of research topics.. We also find that there is a strong linear correlation between the research popularity of bio-entities, and the amount and number of funding grants, respectively. However, the impact of the amount and number of grant funds on the entity research popularity is decreasing. The above results indicate the extensive applicability of entitymetrics in funding research.

A lot of new scientific documents are being published on various platforms every day. It is more and more imperative to quickly and efficiently discover new words and meanings from these documents. However, most of the related works rely on labeled data, and it is quite difficult to deal with unlabeled new documents efficiently. For this, we have introduced an unsupervised method based on sentence patterns and part of speech (POS) sequences. Our method just needs a few initial learnable patterns to obtain the initial terminology tokens and their POS sequences. In this process, new patterns are constructed and can match more sentences to find more POS sequences of terminology. Finally, we use obtained POS sequences and sentence patterns to extract terminology terms in new scientific text. Experiments on paper abstracts from Web of Knowledge show that this method is practical and can achieve a good performance on our test data.

Public messages on the Internet political inquiry platform rely on manual classification, which has the problems of heavy workload, low efficiency, and high error rate. A Bi-directional long short-term memory (Bi-LSTM) network model based on attention mechanism was proposed in this paper to realize the automatic classification of public messages. Considering the network political inquiry data set provided by the BdRace platform as samples, the Bi-LSTM algorithm is used to strengthen the correlation between the messages before and after the training process, and the semantic attention to important text features is strengthened in combination with the characteristics of attention mechanism. Feature weights are integrated through the full connection layer to carry out classification calculations. The experimental results show that the F1 value of the message classification model proposed here reaches 0.886 and 0.862, respectively, in the data set of long text and short text. Compared with three algorithms of long short-term memory (LSTM), logistic regression, and naive Bayesian, the Bi-LSTM model can achieve better results in the automatic classification of public message subjects.

The digital lifecycle encompasses definitive processes for data curation and management, long-term preservation, and dissemination, all of which are key building blocks in the development of a digital library. Maintaining a complete digital lifecycle workflow is vital to the preservation of digital cultural heritage and digital scholarship. This paper considers digital lifecycle programs for digital libraries, noting similarities between the digital and print lifecycles and referring to the example of the Digital Dunhuang project. Only through a systematic and sustainable digital lifecycle program can platforms for cross-disciplinary research and repositories for large aggregations of digital content be built. Moreover, advancing digital lifecycle development will ensure that knowledge and scholarship created in the digital age will have the same chances for survival that print-and-paper scholarship has had for centuries. It will also ensure that digital library users will have effective access to aggregated content across different domains and platforms.

Based on the emerging concept of “Hybrid Intelligence,” this paper aims to explore a new model of human–computer interaction, and deeply research on its development and application of Intelligent Service in the big data environment. It systematically explores the related academic concepts of hybrid intelligence, and establishes its architecture model. The development of hybrid intelligence is faced with cognitive differences, system fragmentation, human–machine digital divide, and other issues. Strengthening the interaction between cognition and perception can be the key to break through the bottleneck. The intelligent service system based on the hybrid intelligent architecture takes knowledge fusion as the core, and “cloud intelligent brain” is making it possible for the human–computer symbiosis driven by hybrid intelligence. The proposed advanced human–computer interaction mode constructs a hybrid intelligent architecture model, enriches the concept system of human–machine hybrid intelligence, and provides a new landing scheme for intelligent services based on complex scenes in the big data environment.

This research provides a systematic analysis of 115 previous literatures on the use of academic social networking sites (ASNs) in scholarly communication. Previous research on the subject has mainly taken a disciplinary and user perspective. This research conceptualizes the use of ASNs in scholarly communication in the space between social interactions and the technologies themselves. Keyword analysis and scoping review approaches have been used to analyze the comprehensive literature in the field. The study found a geographic variation in what motivates academics to use ASNs. Scholar discovery and sharing are the primary driving factors identified in the literature. Four main themes within the research literature are proposed: motivation and uses, impact assessment, features and services, and scholarly big data. The study found that there has been an increase in scholarly big data research in recent years. The paper also discusses the key findings and concepts stated in each theme. This gives academics a better understanding of what ASNs can do and their weaknesses, and identifies gaps in the literature that are worth addressing in future investigations. We suggest that future studies may also extend the existing theoretical framework and epistemological approaches to better predict and clarify the socio-technical dimensions of ASNs use in scholarly communication. In addition, this study has implications for academic and research institutions, libraries and information literacy programs, and future studies on the topic.