Scientific Knowledge Graph Research Articles

Scientific knowledge graph and trend analysis of operation management: A bibliometric analysis

Operation management, which has always been a hot topic in management research for seeking to increase the efficiency of the system using scientific methods. We use the Web of Science database to select the pertinent literature data from 2012 to 2022 with a focus on 10 significant international journals to perform the scoped literature on operation management in this paper. It then conducts a bibliometric analysis of operation management from two perspectives—main direction and development trend—using CiteSpace and VOSviewer. The analysis discovered that between 2012 and 2022, 22952 papers on operation management were published in significant international journals. Of these, 4398 were written by Chinese academics, making up 19.16% of the total. International Journal of Production Research, European Journal of Operational Research, and International Journal of Production Economics were the main venues for Chinese academics' publications. The National Natural Science Foundation of China supported 3446 papers in these journals, or 15.01% of all papers published. A variety of research hotspots are revealed by keyword analysis. The research's findings provide an excellent framework for describing the traits and developments in operation management.

New trends in scientific knowledge graphs and research impact assessment

New trends in scientific knowledge graphs and research impact assessment

Research on the Trends and Features of Enterprise Digital Transformation: Based on the WOS Database

In the context of the digital economy, in order to systematically understand the overall characteristics of the digital transformation of Chinese enterprises, this article conducts a statistical analysis of hotspots and trends in this field, which can provide a useful directional reference for subsequent scholars. Using literature statistical methods, processing literature data based on CiteSpace software, using data mining thinking to analyze scientific metrology knowledge graphs, selecting the enterprise digital transformation research papers published in the Web of Science database from 2008 to 2021 as the research object, Analyze the changes in the volume of articles, research institutions, authors, and keywords. The research shows that after 2018, it is a period of rapid development of enterprise digital transformation; paper publishing institutions are mainly concentrated in institutions and universities that are excellent in engineering and management. Most institutions conduct corresponding research independently, but cooperative research is Future trends; five author research communities constitute the main research strength; the research mainly focuses on digital government, intelligent manufacturing, industrial Internet and supply chain management, and has important theoretical reference value for the development of digital transformation of Chinese enterprises.

Scientific Knowledge Graph of Acupuncture for Migraine: A Bibliometric Analysis from 2000 to 2019.

ObjectiveThis study aims to explore the trend and knowledge mapping of acupuncture for migraine through bibliometrics.MethodsIt retrieved the literature on acupuncture for migraine in the Web of Science database from 2000 to 2019, and then resorted to CiteSpace to conduct bibliometric analysis to attain the knowledge mapping.ResultsThe total number of publications each year has increased year by year, and the average annual growth rate from 2000 to 2009 was 15.57%, while from 2010 to 2019 was 6.35%, with a faster growth rate from 2000 to 2009. According to the cluster analysis of institutions, authors, cited references, and keywords, 10, 7, 12, and 10 categories were gained from 2000 to 2019. The most productive countries, institutions, and authors are the USA and China, Technical University of Munich and Beijing University of Chinese Medicine, Linde K and Liang FR from 2000 to 2019, whose frequency is 119/103, 28/24, and 28/24, respectively. However, the most important of them are Canada, Sichuan University, and Witt CM. Owing to their highest centrality, they are 0.86, 0.54, and 0.27 separately. Moreover, cited references that contributed to the most co-citations are Linde K (2005), yet, the most vital cited reference is Karst M (2001). Keywords such as migraine, acupuncture, headache, pain, and randomized controlled trial are the most frequently used. However, needle acupuncture is the crucial keyword. In the cluster analysis of institutions, authors, cited references, and keywords from 2000 to 2019, the largest cluster categories are #0 migraine prophylaxis, #1 randomized controlled trial, #0 episodic migraine, and #0 topiramate treatment. Then, randomized controlled trials of acupuncture prevention and treatment of migraine are the most important research content in this field.ConclusionThrough the bibliometric analysis of the research on acupuncture for migraine in the Web of Science database in the past 20 years, the trends and the Knowledge Graph of the country, institution, author, cited reference, and the keyword are acquired, which have an important guiding significance for quickly and accurately positioning the key information in the field.

Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations.

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

Visualization and Analysis of Mapping Knowledge Domains for Food Waste Studies

Food waste and loss is a global issue involving ethics, society, the environment, and the economy. However, there is a lack of systematic and visual scientific knowledge and graph methods to study the precedents of this field’s development over time. The article is based on the scientific knowledge graph research of articles published in the past 22 years to review the latest food waste research developments. The study will be conducted from the following perspectives: country/region, institution, author, journal, keyword co-occurrence, and article co-citation. It turns out that in the past eight years, food waste research has grown rapidly. A total of 8298 research articles have been published in 8064 journals and 176 Web of Science (WOS) subject categories. Research shows in the past 20 years. The main research hotspots were anaerobic digestion, biogas production, composting, biological hydrogen production, and innovation in system management methods. In the future, efficient and multitask biological value-added conversion technology, systematization of food-supply-chain decision-making aid models, and research on differences in management strategies may become the frontiers of research.

Bibliometric Analysis of Knowledge Networks and Creativity

Despite the sharp increase in awareness of workplace knowledge networks and creativity research, no attention has been paid to objectively visualizing the evolution of this fast-growing area to complement prior qualitative reviews. This bibliometric analysis involves an examination of 341 global knowledge networks and creativity articles in management-related research in the Web of Science database. Using CiteSpace V visualization literature measurement software, the knowledge map of the knowledge networks and creativity research was drawn using the scientific metrology knowledge graph research method. We conduct Publication time analysis, country/region analysis, journal co-citation analysis, author co-citation, document co-citation, the time-zone visualization analysis on research literature in the field of knowledge networks and creativity, the major researchers and topics in the field of knowledge network, and creativity are clearly presented. Moreover, we summarize the developing trends of knowledge networks and creativity research. On this basis, our review demonstrates the systematic development of literature and identifies trends to advance knowledge networks and creativity research.

Data and code of the article "Can we assess research using open scientific knowledge graphs? A case study within the Italian National Scientific Qualification"

Data and code of the article "Can we assess research using open scientific knowledge graphs? A case study within the Italian National Scientific Qualification"

Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we (i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, (ii) describe an approach for integrating entities and relationships generated by these tools, (iii) show the advantage of such an hybrid system over alternative approaches, and (vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.

Semantic Publication of Agricultural Scientific Literature Using Property Graphs

During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines Semantic Web and natural language processing technologies, which make data understandable by computer agents, empowering the development of final user applications for literature searches. This workflow consists of (1) RDF generation, including metadata and contents; (2) semantic annotation of the content; and (3) property graph population by adding domain knowledge from ontologies, in addition to the previously generated RDF data describing the articles. This pipeline was applied to a set of 127 agriculture articles, generating a knowledge graph implemented in Neo4j, publicly available on Docker. The potential of our model is illustrated through a series of queries and use cases, which not only include queries about authors or references but also deal with article similarity or clustering based on semantic annotation, which is facilitated by the inclusion of domain ontologies in the graph.

Enlightenment of thematic knowledge Map Analysis of Human Resource Management practice to Engineering Management in China

Human resource is contained in the knowledge and ability of employees and employee relationship in the enterprise, and it is a competitive advantage that competitors can not imitate. Effective human resources management practice can promote organizational innovation. This study uses the knowledge graph research method to analyze the journals of the practice theme of human resource management in China. Taking the periodicals included in the core journals from 1994 to 2018 as the research object, using citespace software to visually analyze the existing literature from the literature author, the key words and so on, by drawing the scientific knowledge graph, the present situation and research hotspots of the practice theme of human resource management in China are identified. And this paper puts forward some suggestions on the optimization of human resource management in engineering management.

Visualizing the Knowledge Structure and Research Evolution of Infrared Detection Technology Studies

This paper aims to explore the current status, research trends and hotspots related to the field of infrared detection technology through bibliometric analysis and visualization techniques based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles published between 1990 and 2018 using the VOSviewer and Citespace software tools. Based on our analysis, we first present the spatiotemporal distribution of the literature related to infrared detection technology, including annual publications, origin country/region, main research organization, and source publications. Then, we report the main subject categories involved in infrared detection technology. Furthermore, we adopt literature cocitation, author cocitation, keyword co-occurrence and timeline visualization analyses to visually explore the research fronts and trends, and present the evolution of infrared detection technology research. The results show that China, the USA and Italy are the three most active countries in infrared detection technology research and that the Centre National de la Recherche Scientifique has the largest number of publications among related organizations. The most prominent research hotspots in the past five years are vibration thermal imaging, pulse thermal imaging, photonic crystals, skin temperature, remote sensing technology, and detection of delamination defects in concrete. The trend of future research on infrared detection technology is from qualitative to quantitative research development, engineering application research and infrared detection technology combined with other detection techniques. The proposed approach based on the scientific knowledge graph analysis can be used to establish reference information and a research basis for application and development of methods in the domain of infrared detection technology studies.

Analysis of Spatial-temporal Distribution, Focus Trends and Evolution of Sports Culture Education Research in China

Based on the bibliometric method, 770 papers on sports culture education collected in CSSCI on CNKI since 1998 were visualized, and the scientific knowledge graph was drawn by the CiteSpace V visual analysis software to study the number of papers on sports culture education, scientific research institutions, authors, research focus and evolutionary processes to further gain an overall understanding of the trends and dynamics of sports education research in China, which provides a reference for the direction of sports culture education research. Research conclusions: Firstly, the research on sports culture education in China began in 1998, and the number of publications reached the peak in 2014, which afterwards still remained high despite slight decline. Secondly, normal colleges and universities are the main force for the research on sports culture education. Thirdly, in terms of the evolutionary process, the research on sports culture education continuously keeps eyes on school sports, and transfer its focus to the competitive sports and mass sports at the later stage, indicating that sports culture education is a systematic project involving the entire population and requires the participation of the whole society.

Study on Map Knowledge Domains of Transgenic Maize Based on Citespace

To analyze the hotspots and trend of the study on global transgenic maize, the paper collected literature on transgenic maize from 2001 to 2018 which are included in the core journals in the database of Web science as samples and used Citespace for visual analysis of mapping knowledge domain of studies on transgenic maize, based on the bibliometric method. As a result, 3546 pieces of documents have been sifted out from 3557 pieces of documents which have been retrieved according to the search strategy, the literature are in a stable situation and show a tendency of increase. The paper analyzed the scientific knowledge graphs on co-authors analysis network, co-institutions analysis network, author co-citation analysis network, citation analysis network and keyword co-appearance network by using Citespace software. Therefore, in cluster analysis, it is found that in the study on transgenic maize, Chinese scholars and institutions hold an advantage in quantity while the citation of foreigners’literature is higher than Chinese. Besides, the focus of global research is mainly about bacillus thuringiensis, arabidopsis thaliana, protein, gene expression, transformation, resistant, etc.