Research Trends on the Corporate Expatriates: Analysis of research topics found in domestic and overseas academic journals published

2017 ◽  
Vol 19 (2) ◽  
pp. 159-180
Author(s):  
Hyun Jong Shin ◽  
Sung Min Park
Keyword(s):  
Research Trends ◽  
Academic Journals ◽  
Research Topics

scholarly journals CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

2021 ◽  
Author(s):  
Angelo Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Computer Science ◽  
Digital Libraries ◽  
Gold Standard ◽  
Research Trends ◽  
Alternative Methods ◽  
Relevant Research ◽  
Research Topics ◽  
Research Papers ◽  
Research Areas ◽  
Unsupervised Approach

AbstractClassifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

scholarly journals A Scientometric Analysis of Publications Related to Predictive Medicine

2020 ◽  
pp. paper81-1-paper81-12
Author(s):  
Aida Khakimova ◽  
Dongxiao Gu ◽  
Oleg Zolotarev ◽  
Maria Berberova ◽  
Michael Charnine
Keyword(s):  
Noun Phrases ◽  
Subject Area ◽  
Research Trends ◽  
Free Software ◽  
Scientometric Analysis ◽  
Research Topics ◽  
Pubmed Database ◽  
Preventive And Personalized Medicine ◽  
New Research ◽  
And Personalized Medicine

Due to the increasing popularity of new research in medicine thisstudy was conducted to determine recent research trends of predictive, preventive and personalized medicine (PPM). We identified the terms relevant to PPM using own search engine based on neural network processing in PubMed database. We extracted initially about 15000 articles. Then we carried out the statistical analysis for identifying research trends. The article presents the results of solving the problem of evaluating research topics at the level of thematic clusters in a separate subject area. An approach based on the analysis of article titles has been implemented. Identification of terms, connections between them and thematic clustering were carried out using the free software VOSViewer, which allows to extract terms in the form of noun phrases, as well as to cluster them.

Laser Assisted Machining: An Overview

1997 ◽  
Vol 119 (4B) ◽  
pp. 766-769 ◽  
Author(s):  
G. Chryssolouris ◽  
N. Anifantis ◽  
S. Karagiannis
Keyword(s):  
Scientific Work ◽  
Research Trends ◽  
Experimental Investigations ◽  
Laser Machining ◽  
Relevant Research ◽  
Research Topics ◽  
Laser Technology ◽  
Laser Assisted Machining ◽  
Recent Developments ◽  
Potential Applications

Since laser technology has considerable synergy with machining technologies, Laser Machining (LM) and Laser Assisted Machining (LAM) are relevant research topics. This paper attempts to give an overview of recent developments and research trends. Although scientific work on this area has contributed to the understanding of the process, there are still unresolved problems regarding the limitations of the techniques, optimum machining conditions, etc. The outcome of experimental investigations on LAM shows potential applications for this process but there are several issues to be resolved.

Analysis of Research Trends about Rural Landscape Published in Korean Academic Journals

2015 ◽  
Vol 21 (3) ◽  
pp. 121-129 ◽  
Author(s):  
Duk-cho An ◽  
hyung-eun Oh ◽  
Ji-hoon Park ◽  
Eun-ji Kang ◽  
Yong-geun Kim
Keyword(s):  
Research Trends ◽  
Rural Landscape ◽  
Academic Journals

scholarly journals Empirical Research on Pedagogical Dictionary Use in Recent 30 Years

2019 ◽  
Vol 8 (6) ◽  
pp. 103
Author(s):  
Qian Li
Keyword(s):  
Data Analysis ◽  
Empirical Research ◽  
Empirical Studies ◽  
Research Trends ◽  
Descriptive Statistics ◽  
Research Trend ◽  
Research Topics ◽  
The Past ◽  
Using Data ◽  
Dictionary Use

Research on dictionary use is a relatively new field in lexicography. Among them, the empirical studies which were few before 1990s has gained ground over recent three decades. Using data of 35 articles from International Journal of Lexicography (1987–2017), this study renders an analysis of the empirical research trends in the field of dictionary use. The analysis mainly focuses on the research topics, research methodology, and the changes that have occurred in the field. The results show that while some hot topics (e.g., the effectiveness of dictionary use or of certain dictionary information) have remained popular over the past two decades, some topics, e.g., the exploration of dictionary using process have received an increasing attention, but some others, e.g., the investigation on habits and needs of dictionary use, have witnessed a decrease of interest recently. Furthermore, researchers have improved the methodological standards for recent studies. As for data analysis, more complicated statistic approaches, rather than pure descriptive statistics, have been adopted. Finally, based on the analysis on previous studies, this paper offers suggestions for further research trend.

scholarly journals How are topics born? Understanding the research dynamics preceding the emergence of new areas

2016 ◽  
Author(s):  
Angelo A Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Early Detection ◽  
State Of The Art ◽  
Early Stage ◽  
Research Area ◽  
Research Trends ◽  
Research Topics ◽  
Related Research ◽  
Research Areas ◽  
Institutional Funding ◽  
New Research

The ability to recognise new research trends early is strategic for many stakeholders, such as academics, institutional funding bodies, academic publishers and companies. While the state of the art presents several works on the identification of novel research topics, detecting the emergence of a new research area at a very early stage, i.e., when the area has not been even explicitly labelled and is associated with very few publications, is still an open challenge. This limitation hinders the ability of the aforementioned stakeholders to timely react to the emergence of new areas in the research landscape. In this paper, we address this issue by hypothesising the existence of an embryonic stage for research topics and by suggesting that topics in this phase can actually be detected by analysing diachronically the co-occurrence graph of already established topics. To confirm our hypothesis, we performed a study of the dynamics preceding the creation of novel topics. This analysis showed that the emergence of new topics is actually anticipated by a significant increase of the pace of collaboration and density in the co-occurrence graphs of related research areas. These findings are very relevant to a number of research communities and stakeholders. Firstly, they confirm the existence of an embryonic phase in the development of research topics and suggest that it might be possible to perform very early detection of research topics by taking into account the aforementioned dynamics. Secondly, they bring new empirical evidence to related theories in Philosophy of Science. Finally, they suggest that significant new topics tend to emerge in an environment in which previously less interconnected research areas start cross-fertilising.

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