Global Journal of Engineering Sciences (GJES)
The
Use of Dissimilarity Measures for the Study of Evolution in Scientific Fields
Authored by Lukun Zheng
Abstract
One of
the key issues in evolution of scientific field is to quantify the
dissimilarity between two collections of scientific publications in literature.
Many existing works study the evolution based on one or two dissimilarity
measures, despite the fact that there are many different dissimilarity
measures. Finding the appropriate dissimilarity measures among such a
collection of choices is of fundamental importance to the study of scientific
evolution. In this paper, we attempt to study the use of dissimilarity measures
in scientific evolution.
Keywords: Scientific evolution; Dissimilarity
measures; Principal component analysis (PCA); Dissimilarity integration
Introduction
The
scientific theory of evolution by natural selection began with Charles Darwin’s
On the Origin of Species published in 1859 [1,2]. Evolution as a scientific
theory has been used in many other disciplines as well, including medicine
[3,4], psychology [5,6], anthropology [6], forensics [7], agriculture [8], and
other socialcultural applications [9,10]. In this paper, we aim at studying the
evolution of scientific fields by investigating their developmental trends
shown in scientific literature [11].
Evolution
in scientific fields has been drawing attention among researchers and
scientists in recent years. One of the main issues in evolution of scientific
field is to quantify the dissimilarity between two collections of scientific
publications in literature. The temporal evolution of scientific research can
be observed in retrospective studies in many fields. What is more, the
“evolution map” of scientific fields helps us understand the nature of
scientific development and the relative importance of different topics or
publications [11]. Innovations and scientific breakthroughs keep on emerging,
leading to new or improved technology and scientific findings, which, then,
shape new developmental trends in various research areas. One of the main
issues in evolution of scientific field is to quantify the dissimilarity
between two collections of scientific publications in literature. There are
many dissimilarity measures encountered in many different areas such as
biology, computer science, mathematics, psychology, statistics, etc. Finding
the appropriate measures among such a collection of choices is of fundamental
importance to pattern classification, clustering, and information retrieval
problems [12-14].
Integration of Dissimilarity Measures
Over
the years, there have been many approaches for measuring scientific evolution.
For instance, Vargas-Quesada et al. [15] introduced a graphic representation of
the intellectual structures in the form of scientograms using scientometric
information such as cocitation network from a certain scientific domain. Their
approach allows one to detect patterns and tendencies of scientific evolution
in a scientific domain through network visualizations. Dias et al. [16] used an
information-theoretic measure of linguistic similarity to investigate the
organization and evolution of scientific fields based on the 20,000 most
frequent words from the abstracts of the papers considered excluding a list of
stop words. Jurgens et al. [17] proposed a method on measuring scientific
evolution by studying how scientific works frame their contributions through
different types of citations and how this framing affects the field. Frank et
al. [18] used the Microsoft Academic Graph to study the bibliometric evolution
of AI research and its related fields from 1950 to 2019. The problem with these
works is that they only adopted few (mostly just one single) dissimilarity
measures in their study, ignoring the fact that there are many such measures
[19,20].
In
recent years, many attempts on integrating different dissimilarity measures
have been made and they show promising results in various areas such as image
classification [21], text categorization [22], and patten recognition [23].
Most of these works combine dissimilarity measures using a weighted sum with
weights determined using different algorithms such as trialand- error method, and
cross validation. In the study of scientific evolution, Zheng & Jiang [24]
proposed a novel approach for the integration of twelve dissimilarity measures
based on keywords distributions in scientific fields using principal component
analysis (PCA). They collected a collection of bibliographic records of
articles from four selected scientific fields published from 1991 to 2019 and
obtain the yearly keyword distributions and calculated the values of twelve
dissimilarity measures between the keyword distributions for each pair of
successive years. Then PCA is unutilized to combine these dissimilarity
measures. Their results show a decreasing trend for the evolution between two
successive years in all chosen fields during the time 1991-2019 [25,26].
Conclusion
Most
of the studies on scientific evolution have been limited to the use of single
measures. Considering the successful applications of integrating dissimilarity
measures, it would be a good idea to study the use of different integration
techniques (e.g., ensemble methods) on scientific evolution. We believe that
more efforts are needed to systematically study various properties of different
dissimilarity measures in the study of scientific evolution. Future works are
also needed to explore and compare the advantages and limitations of different
integration approaches in this area.
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