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

2017 ◽  
Vol 3 ◽  
pp. e119 ◽  
Author(s):  
Angelo A. Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Research Area ◽  
Research Trends ◽  
Embryonic Stage ◽  
Relevant Research ◽  
Research Topics ◽  
Research Areas ◽  
Institutional Funding ◽  
Community Of Researchers ◽  
Number Of Publications ◽  
New Research

The ability to promptly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the ‘parents’ of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise.

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.

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.

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.

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.

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.

scholarly journals AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry

2021 ◽  
pp. 1-43
Author(s):  
Simone Angioni ◽  
Angelo Salatino ◽  
Francesco Osborne ◽  
Diego Reforgiato Recupero ◽  
Enrico Motta
Keyword(s):  
Computer Science ◽  
Knowledge Graph ◽  
Industry Dynamics ◽  
Relevant Research ◽  
Research Topics ◽  
Industrial Sectors ◽  
Scholarly Data ◽  
Automatic Pipeline ◽  
Different Parts

Abstract Academia and industry share a complex, multifaceted, and symbiotic relationship. Analysing the knowledge flow between them, understanding which directions have the biggest potential, and discovering the best strategies to harmonise their efforts is a critical task for several stakeholders. Research publications and patents are an ideal medium to analyze this space, but current datasets of scholarly data cannot be used for such a purpose since they lack a high-quality characterization of the relevant research topics and industrial sectors. In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to the research topics drawn from the Computer Science Ontology. 5.1M publications and 5.6M patents are further characterized according to the type of the author’s affiliations and 66 industrial sectors from the proposed Industrial Sectors Ontology (INDUSO). AIDA was generated by an automatic pipeline that integrates data from Microsoft Academic Graph, Dimensions, DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. It is publicly available under CC BY 4.0 and can be downloaded as a dump or queried via a triplestore. We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results.

National and international trends in library and information science research: A comparative review of the literature

IFLA Journal ◽  
2019 ◽  
Vol 46 (3) ◽  
pp. 234-249 ◽  
Author(s):  
Mallikarjun Dora ◽  
H. Anil Kumar
Keyword(s):  
Information Science ◽  
Science Research ◽  
International Perspective ◽  
Research Trends ◽  
Research Trend ◽  
Library And Information Science ◽  
Research Topics ◽  
Research Papers ◽  
International Trends ◽  
Comparative Review

The study is an attempt to understand the trends in LIS research by analyzing published literature on the topic. The study identifies and analyses 39 research papers on LIS research trends in various countries, three papers on LIS research trends in regional countries and 13 papers on LIS research trends with an international perspective. The findings of the study reveal that there is a similarity among various countries as far as the LIS research topics are concerned but with a different focus at different periods. While understanding international research trends in LIS, it was interesting to note that the research trend in China was similar to the worldwide research trend while the pattern in other countries differed.

Computer Science Unplugged: A Systematic Literature Review

2021 ◽  
pp. 004723952110188
Author(s):  
Ali Battal ◽  
Gülgün Afacan Adanır ◽  
Yasemin Gülbahar
Keyword(s):  
Literature Review ◽  
Computer Science ◽  
Systematic Literature Review ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Inclusion Criteria ◽  
Relevant Research ◽  
Tangible Programming ◽  
Review Procedure ◽  
Research Studies

The computer science (CS) unplugged approach intends to teach CS concepts and computational thinking skills without employing any digital tools. The current study conducted a systematic literature review to analyze research studies that conducted investigations related to implementations of CS unplugged activities. A systematic review procedure was developed and applied to detect and subsequently review relevant research studies published from 2010 to 2019. It was found that 55 research studies (17 articles + 38 conference proceedings) satisfied the inclusion criteria for the analysis. These research studies were then examined with regard to their demographic characteristics, research methodologies, research results, and main findings. It was found that the unplugged approach was realized and utilized differently among researchers. The majority of the studies used the CS unplugged term when referring to “paper–pencil activities,” “problem solving,” “storytelling,” “games,” “tangible programming,” and even “robotics.”

scholarly journals Detecting bursty terms in computer science research

2019 ◽  
Vol 122 (1) ◽  
pp. 681-699 ◽  
Author(s):  
E. Tattershall ◽  
G. Nenadic ◽  
R. D. Stevens
Keyword(s):  
Computer Science ◽  
Historical Data ◽  
Science Research ◽  
Detection Algorithm ◽  
Trend Detection ◽  
Free Text ◽  
Research Topics ◽  
Computer Science Research ◽  
Insight Into ◽  
Over Time

AbstractResearch topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.

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