=Paper=
{{Paper
|id=Vol-2864/paper26
|storemode=property
|title=An Algorithm for Topic Modeling of Researchers Taking Into Account Their Interests in Google Scholar Profiles
|pdfUrl=https://ceur-ws.org/Vol-2864/paper26.pdf
|volume=Vol-2864
|authors=Serhiy Shtovba,Mykola Petrychko
|dblpUrl=https://dblp.org/rec/conf/cmis/ShtovbaP21
}}
==An Algorithm for Topic Modeling of Researchers Taking Into Account Their Interests in Google Scholar Profiles==
https://ceur-ws.org/Vol-2864/paper26.pdf
An algorithm for topic modeling of researchers taking into
account their interests in Google Scholar profiles
Serhiy Shtovbaa and Mykola Petrychkob
a
Vasyl’ Stus Donetsk National University, 600-richchia str., 21, Vinnytsia, 21021, Ukraine
b
Vinnytsia National Technical University, Khmelnytske Shose, 95, Vinnytsia, 21021, Ukraine
Abstract
An algorithm for topic modeling of researchers based on their interests from Google
Scholar’s profiles is proposed. As topics for modeling, we took research groups from
research classification system ANZSRC – Australian and New Zealand Standard Research
Classification. Researchers’ distribution to research groups is found based on their interests’
usage statistics in categorized publications from Dimensions. Topic modeling is conducted
accordingly to principles of statistical support, multi-labeling, noise filtering, ignoring stop-
words, solidarities, focusing, compactness and research groups’ interactions. We compare
topic modeling based on data with low level of information from researchers’ profiles in
Google Scholar with topic modeling based on a few dozen authored publications categorized
by Dimensions. Comparison is made by modified Czekanowski metric that takes into account
the interaction between research groups. By comparing the results of topic modeling based on
different sources of initial information a good match was found. It allows to use the proposed
algorithm as the intellectual core of information technology in regards to scientific staff, in
particular, for the selection of candidates as opponents of a dissertation, as reviewers for
research projects, for forming a team to collaborate on mutual research projects etc.
Keywords
topic modeling, Google Scholar, Dimensions, ANZSRC, researcher’s profile, research
interests, research group, Czekanowski metric, Jaccard index.
1. Introduction
Google Scholar aggregates the most volumetric collection of researchers’ profiles. The most used
information from Google Scholar profiles is citations. It, for example, is used as primary information
for university ranking in Webometrics. Several studies, in particular [1, 2], are concerned with
comparing concordance of Google Scholar citations with different scientometrics systems such as
Scopus, Web of Science, Dimensions and others, that use only meta-information from publishers. A
researcher’s profile in Google Scholar contains not only publications and their citations, but also other
information. In particular, a researcher provides his or her interests. A researcher chooses the interests
in a loose manner without any limitations. Google Scholar provides a web interface to search
researchers by an interest. However, the results are formed by literal coincidence. That is why the
results for fuzzy set and fuzzy sets are different; the same applies for synonymous interests such as
fuzzy evidence and fuzzy inference. Moreover, Google Scholar does not take into account an
interconnection of the interests, that is the search by an interest is done independently and isolated.
Given that, the search and analytical services that provide information about many researchers in
Google Scholar are relatively straightforward.
The goal of this paper is topic modeling of researchers based on their interests from Google
Scholar. Methods that process a researcher’s interests from Google Scholar profile are not studied
CMIS-2021: The Fourth International Workshop on Computer Modeling and Intelligent Systems, April 27, 2021, Zaporizhzhia, Ukraine
EMAIL: shtovba@donnu.edu.ua (S. Shtovba); mpetrychko@vntu.edu.ua (M. Petrychko)
ORCID: 0000-0003-1302-4899 (S. Shtovba); 0000-0001-6836-7843 (M. Petrychko)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
well. We identified only two relevant publications. One of them is [3], it describes a recommendation
system that recommends supervisors based on some information and interests of candidates from
Google Scholar profiles as well. Another paper [4] presents an information technology that
synthesizes a research profile of institute or research laboratory. It also uses interests of researchers
from their profiles at Google Scholar. Articles [3, 4] are based on pairwise comparison using cosine
similarity metric between researcher and a set of keywords from a given topic. Such a topic in [3] is
an article at Wikipedia. Unlike these methods, we strive to categorize researchers by a given research
classification system, that is to assign a research group to each of them.
Automatic researchers’ categorization is usually done as a result of generalizing the topics of their
publications. One of the methods for this is presented at [5]. The authors present a statistical model
“Author-Topic” that is based on topic modeling model Latent Dirichlet Allocation [6]. This model
represents a researcher as a distribution over some abstract topics. The topics are clusters of similar
words. One of the drawbacks of this model is low interpretation of the topics because they are formed
by words frequency in a document. To improve the interpretation another model “Author-Subject-
Topic” is proposed in [7]. This model additionally uses a research specialty that is defined by journal
in which an analyzed publication is published. In [8] another improvement of “Author-Topic” model
is presented – “Author-Persona-Topic” model. In this model rather than representing all researcher’s
documents as single topic distribution, authors group all documents into different clusters, each with
its own topic distribution. These clusters represent “personas” under which an author writes.
Apart from topic modeling methods there are also methods based on word embedding. They
generally perform better than topic models because they can incorporate semantic relationships. One
of the most popular models of embedding is word2vec [13]. It is used in [9] as a part of similarity
metric between researchers using their publications. They assess the similarity between words that
comprise publications of different researchers using representation of words defined by word2vec.
The authors of [10] use publications’ titles as source information for solving the problem of
collaboration recommendation. The words from the titles are represented as vectors using word2vec.
These vectors are then clustered using k-means to partition researchers into different academic
domains. The representation of a researcher is further improved by using his co-authorship and the
random walk method to find his influence in different domains. In [11] authors represent a researcher
as a set of documents he/she has written. The words of the documents are defined as vectors trained
by word2vec model. These representations are then used to solve the problem of expert finding by
utilizing a restricted convolutional neural network. In [12] a researcher is represented as a
concatenation of all his/hers abstracts. Each word in the concatenation is then represented as a vector
from word2vec model to solve the problem of reviewer recommendation.
Analyzed methods assume to have enough number of publications for a given researcher with
selected keywords. At the same time, they do not account for the fact that co-author contribution is
sometimes relative to a small subset of the paper keywords. A researcher, especially a young one,
may have only a few publications that may not be enough for a valid categorization. On the other
hand, the researcher can manually specify at the profile a set of keywords that describe his (or her)
activities. As the time goes on a researcher may change his research direction, for example, move to
another laboratory or another project. Given that there is no change when a researcher is categorized
based on his publications, categorization based on his keywords may find these changes. By taking
that in mind, we study the topic modeling based on actual interests that a researcher specified by
himself (or herself) at the current moment.
2. Problem statement
We use the following notations:
W w1 , w2 ,..., wn is a set of keywords that are equal to researcher interests in Google Scholar
profile;
T t1 , t 2 ,..., t m is a set of research topics from a research classification system;
D1 , D2 ,..., Dm is a set of topic-marked collections of texts; each collection contains only
publications from topics t1 , t 2 ,..., t m , respectively;
B D1 D2 ... Dm is the general collection of topic-marked texts; each element of B belongs
to one or more topics from the set T ;
R ( D, T ) D T is a relation that describes membership of a publication to topic-marked
collections.
The problem is to find out topics from T that correspond to the set of interests W . The results of
~
mapping W T is a fuzzy set W defined on the universal set of topics T as follows:
~ (t ) (t ) (t )
W W 1 , W 2 ,..., W m ,
t1 t2 tm
_____
where W (t p ) 0,1 denotes membership degree of the set of interests W to topics t p , p 1, m .
~
We set the following restrictions on W :
~
1) the cardinality of the fuzzy set support must be small 1 sup W Tmax , for example, with
Tmax 2,3,4 a researcher will be assigned only to a few topics;
2) t 1 , which is equivalent to the topic modeling regularization condition.
____
W p
p 1, m
3. Data acquisition and preprocessing
We use a researcher’s profile from Google Scholar to get the keywords. For example, in Figure 1
we have a researcher’s profile with three keywords that are marked with blue color. For this
researcher: w1 " Computational Intelligence" ; w 2 " Fuzzy Logic" ; w3 " Artificial Intelligence" .
The order of keywords in the set W is not important, this corresponds to the bag of words model.
Interests often complement each other thus making their research topics more focused. To take that
into account we synthesize additional keywords defined as pairs of initial interests. Interests in a pair
are combined by a logical operation AND. For researcher from Figure 1 additional keywords are
defined as follows:
w 4 " Computational Intelligence" AND " Fuzzy Logic" ;
w5 " Computatio nal Intelligence" AND " Artificial Intelligence"
w6 " Fuzzy Logic" AND " Artificial Intelligence"
If a researcher’s profile has 3 interests, additional 3 keywords are synthesized, if it has 4 interests
then 6 additional are synthesized etc.
Figure 1: An example of a researcher’s profile with 3 interests
For researchers’ topic modeling, we need to choose a research classification system. There are a
lot of them, but when choosing we take into account not only their semantic advantages and
disadvantages, but also that there is an information system with this research classification system that
has available search services. In addition, we require that the information system must indexes a large
number of categorized publications over all research. The information system that satisfies these
requirements is Dimensions.
Dimensions indexes more than 110M of publications. All publications are categorized by the two-
level variant of Australian and New Zealand Standard Research Classification (ANZSRC) with 22
research divisions and 154 research groups (Table 1). In this work we use the research groups to
model a researcher’s interests.
Table 1.
Research classification system ANZSRC, that is used in Dimensions
Research Division Research Group
Mathematical A1 ‐ Pure Mathematics; A2 ‐ Applied Mathematics; A3 ‐ Numerical and
Sciences Computational Mathematics; A4 – Statistics; A5 ‐ Mathematical Physics
Physical Sciences B1 ‐ Astronomical and Space Sciences; B2 ‐ Atomic, Molecular, Nuclear,
Particle and Plasma Physics; B3 ‐ Classical Physics; B4 ‐ Condensed Matter
Physics; B5 ‐ Optical Physics; B6 ‐ Quantum Physics; B7 ‐ Other Physical
Sciences
Chemical Sciences C1 ‐ Analytical Chemistry; C2 ‐ Inorganic Chemistry; C3 ‐ Macromolecular and
Materials Chemistry; C4 ‐ Medicinal and Biomolecular Chemistry; C5 ‐ Organic
Chemistry; C6 ‐ Physical Chemistry (incl. Structural); C7 ‐ Theoretical and
Computational Chemistry; C8 ‐ Other Chemical Sciences
Earth Sciences D1 ‐ Atmospheric Sciences; D2 ‐ Geochemistry; D3 ‐ Geology; D4 ‐
Geophysics; D5 ‐ Oceanography; D6 ‐ Physical Geography and Environmental
Geoscience; D7 ‐ Other Earth Sciences;
Environmental E1 ‐ Ecological Applications; E2 ‐ Environmental Science and Management;
Sciences E3 ‐ Soil Sciences; E4 ‐ Other Environmental Sciences;
Biological Sciences F1 ‐ Biochemistry and Cell Biology; F2 ‐ Ecology; F3 ‐ Evolutionary Biology; F4 ‐
Genetics; F5 ‐ Microbiology; F6 – Physiology; F7 ‐ Plant Biology; F8 ‐ Zoology;
F9 ‐ Other Biological Sciences
Agricultural and G1 ‐ Agriculture, Land and Farm Management; G2 ‐ Animal Production; G3 ‐
Veterinary Sciences Crop and Pasture Production; G4 ‐ Fisheries Sciences; G5 ‐ Forestry Sciences;
G6 ‐ Horticultural Production; G7 ‐ Veterinary Sciences; G8 ‐ Other
Agricultural and Veterinary Sciences
Information and H1 ‐ Artificial Intelligence and Image Processing; H2 ‐ Computation Theory
Computing Sciences and Mathematics; H3 ‐ Computer Software; H4 ‐ Data Format; H5 ‐
Distributed Computing; H6 ‐ Information Systems; H7 ‐ Library and
Information Studies; H8 ‐ Other Information and Computing Sciences
Engineering I1 ‐ Aerospace Engineering; I2 ‐ Automotive Engineering; I3 ‐ Biomedical
Engineering; I4 ‐ Chemical Engineering; I5 ‐ Civil Engineering; I6 ‐ Electrical
and Electronic Engineering; I7 ‐ Environmental Engineering; I8 ‐ Food
Sciences; I9 ‐ Geomatic Engineering; I10 ‐ Manufacturing Engineering; I11 ‐
Maritime Engineering; I12 ‐ Materials Engineering; I13 ‐ Mechanical
Engineering; I14 ‐ Resources Engineering and Extractive Metallurgy; I15 ‐
Interdisciplinary Engineering; I16 ‐ Other Engineering
Technology J1 ‐ Agricultural Biotechnology; J2 ‐ Environmental Biotechnology; J3 ‐
Industrial Biotechnology; J4 ‐ Medical Biotechnology; J5 ‐ Communications
Technologies; J6 ‐ Computer Hardware; J7 – Nanotechnology; J8 ‐ Other
Technology
Medical and Health K1 ‐ Medical Biochemistry and Metabolomics; K2 ‐ Cardiorespiratory
Sciences Medicine and Haematology; K3 ‐ Clinical Sciences; K4 ‐ Complementary and
Alternative Medicine; K5 ‐ Dentistry; K6 ‐ Human Movement and Sports
Science; K7 – Immunology; K8 ‐ Medical Microbiology; K9 – Neurosciences;
K10 ‐ Nursing; K11 ‐ Nutrition and Dietetics; K12 ‐ Oncology and
Carcinogenesis; K13 ‐ Ophthalmology and Optometry; K14 ‐ Paediatrics and
Reproductive Medicine; K15 ‐ Pharmacology and Pharmaceutical Sciences;
K16 ‐ Medical Physiology; K17 ‐ Public Health and Health Services; K18 ‐ Other
Medical and Health Sciences;
Research Division Research Group
Built Environment L1 ‐ Architecture; L2 – Building; L3 ‐ Design Practice and Management; L4 ‐
and Design Engineering Design; L5 ‐ Urban and Regional Planning; L6 ‐ Other Built
Environment and Design;
Education M1 ‐ Education Systems; M2 ‐ Curriculum and Pedagogy; M3 ‐ Specialist
Studies In Education; M4 ‐ Other Education
Economics N1 – Economic Theory; N2 – Applied Economics; N3 – Econometrics; N4 –
Other Economics
Commerce, O1 ‐ Accounting, Auditing and Accountability; O2 ‐ Banking, Finance and
Management, Investment; O3 ‐ Business and Management; O4 ‐ Commercial Services; O5 –
Tourism and Marketing; O6 – Tourism; O7 ‐ Transportation and Freight Services;
Services
Studies in Human P1 ‐ Anthropology; P2 ‐ Criminology; P3 ‐ Demography; P4 ‐ Human
Society Geography; P5 ‐ Policy and Administration; P6 ‐ Political Science; P7 ‐ Social
Work; P8 ‐ Sociology; P9 ‐ Other Studies In Human Society
Psychology and Q1 ‐ Psychology; Q2 ‐ Cognitive Sciences; Q3 ‐ Other Psychology and Cognitive
Cognitive Sciences Sciences;
Law and Legal R1 – Law; R2 ‐ Other Law and Legal Studies
Studies
Studies in Creative S1 ‐ Art Theory and Criticism; S2 ‐ Film, Television and Digital Media; S3 ‐
Arts and Writing Journalism and Professional Writing; S4 ‐ Performing Arts and Creative
Writing; S5 ‐ Visual Arts and Crafts; S6 ‐ Other Studies In Creative Arts and
Writing
Language, T1 ‐ Communication and Media Studies; T2 ‐ Cultural Studies; T3 ‐ Language
Communication and Studies; T4 – Linguistics; T5 ‐ Literary Studies; T6 ‐ Other Language,
Culture Communication and Culture
History and U1 ‐ Archaeology; U2 ‐ Curatorial and Related Studies; U3 ‐ Historical Studies;
Archaeology U4 ‐ Other History and Archaeology
Philosophy and V1 ‐ Applied Ethics; V2 ‐ History and Philosophy of Specific Fields; V3 ‐
Religious Studies Philosophy; V4 ‐ Religion and Religious Studies; V5 ‐ Other Philosophy and
Religious Studies
A query to Dimensions is formed separately by each element of the set W. If an element is a
phrase, then it is surrounded by quotes. As a search scope, we use Title and Abstract and we search
only the last 5 years – 2016 – 2020. An example of a search result for the query “fuzzy logic” is
presented in Figure 2. For each research division and research group there is a number of publications
that has the query mentioned in either the title or abstract. The results are sorted by the number of
publications descending. We can also find the overall number of publications for each research
division and group, that is without any query.
4. Topic modeling algorithm
We perform the topic modeling of researchers based on the following principles:
the principle of statistical support – the more publications from a specific research group a
given keyword contains, the more membership degree of this keyword to this research group
is;
the principle of multi-labeling – a keyword can belong to a few research groups;
the principle of noise filtering – we ignore research groups with low membership degree to a
given keyword;
the principle of ignoring stop-words – we ignore keywords that appear in a very large number
of publications;
the principle of solidarities – the more keywords belong to the same research group the larger
the chance that the researcher belongs to this research group;
the principle of focusing – if a topic-marked collection of publications contains a few
keywords of a researcher at once then the chances to assign this researcher to the respected
topic increase;
the principle of compactness – a researcher can only be assigned to a few research groups;
the principle of research groups interaction – when cutting the tail of topic distribution, the
contribution of minor research groups is redistributed on leaders by taking into account their
similarity.
Figure 2: The results from Dimensions by search query “fuzzy logic” for the period 2016‐2020
We propose an algorithm to implement the proposed principles that consists of 3 stages. On the
first stage the set of queries based on keywords and their combination is formed. We use only pairs of
keywords because the results using triples of keywords are often empty and increase the processing
time. The second stage performs topic modeling by each query separately. Research groups are
chosen by the frequency of mentions at a topic-marked collection. Stop-words and noise are filtered
by the frequency of mentions in research groups at all topic-marked collections. The minor research
groups are left out using cumulative principle, by cutting the tail of the distribution. On the third stage
all membership degrees of queries are averaged, the resulted distribution is cut and research groups
with the low membership degree are dropped. To ensure compactness we only allow 1 to 4 research
groups.
%Topic modeling algorithm
% #1 – creating the set E of search queries from the keywords
E=W
for i=1:length(W)
for j=i:length(W)
E={E; [‘“’ W(i) ‘AND’ W(j) ‘”’] }
end
end
% #2 – compute membership degrees to research groups by each query
< Find the number of publications at each topic‐marked collection
N=[N(1), N(2), …, N(m)] >
Counter=0 % the counter of successful query responses
for i=1:length(E)
< Find Q – the number of publications D, that contain E{i} >
If Q>Threshold_SW continue % stop‐words
end
If Q
% Ignore topics with a low number of publications:
indeх=find(t
% Ignore research groups with contribution lower than Tail_1:
Gamma(Rejected)=0
Gamma=Gamma./sum(Gamma) % norm to be in [0, 1]
Counter=Counter+1
Mu(Counter)=Gamma
end
If Counter==0
return (‘Unsuccessful’)
end
% #3 – compute membership degrees using all queries
Mu_mean=mean(Mu) % averaging all successful queries
< Form leaders of research groups that have cumulative contribution Mu_mean not
lower than Tail_2. We restrict the number of leaders to be at most 6 with the
largest cumulative contribution. If we have more than 6 leaders their numbers
will be in the vector Rejected >
% Ignore research groups with contribution lower than Tail_2:
Mu_mean(Rejected)=0
Mu_mean= Mu_mean./sum(Mu_mean) % norm to be in [0, 1]
% Current number of research groups:
Current_N_fields=sum(Mu_mean>0)
% Set the max number of research groups for a researcher:
T_max=min(4, Counter+1)
< Find Mu_worst – the smallest membership degree among the leaders >
while (Current_N_fields>T_max OR Mu_worst
Current_N_fields=Current_N_fields‐1;
Mu_mean= Mu_mean./sum(Mu_mean) % norm to be in [0, 1]
< Find Mu_worst – the smallest membership degree among chosen
research groups >
end
On the last stage of the algorithm when dropping a minor research group its contribution is
redistributed to other research groups based on the similarity defined at [14, 15]. For example, let us
say that on an intermediate stage a researcher is assigned to research groups in the following way:
~ 0.5 0.2 0.2 0.1
W , , , . Let us drop the minor group O4. For this, first using method from [14, 15]
H 6 O5 O 6 O 4
we compute Jaccard indexes between O4 and other research groups. For the data from 2016 – 2020
they are:
J O 4, H 6 0 ;
J O 4, O5 0.13 ;
J O 4, O6 0.22 .
By taking into account the similarity, the contribution of the minor specialty O4 is redistributed in
the following way:
~ 0.5 0 0.1 0.2 0.13 0.1 0.2 0.22 0.1
W , , .
H6 O5 O6
As a result, we get:
~ 0.5 0.213 0.222
W , , .
H 6 O5 O6
After norming to be in [0,1] we have:
~ 0.535 0.228 0.237
W , , .
H6 O5 O6
5. Checking example
Let us illustrate how the algorithm works using as an example topic modeling of the researcher
from Figure 1. Using three interests, we form six queries. Figure 3 shows frequency of queries at
topic-marked collections. Figure 4 shows the results after cutting the first tail of the distribution. Next,
we average by all queries (Figure 5) and cut the tail of the distribution (Figure 6). The resulting
distribution is overfilled due to the broad usage of interests for the given researcher. To make the
results more focused the final stage of the algorithm reduces the number of research groups to 2
(Figure 7). As a result, we get that a researcher with interests at artificial intelligence and neural
networks has the largest membership degree in research groups H1 – Artificial Intelligence and Image
Processing with membership degree 0.441 and H2 – Computation Theory and Mathematics with
membership degree 0.559. Such a categorization of the researcher does not contradict with the
authors’ viewpoint. The example shows that even with two initial keywords the proposed algorithm
finds a good enough membership relation between the researcher and research groups.
Figure 3: Initial membership distribution of each interest to research groups
Figure 4: Distributions after the first noise filtering
Figure 5: Averaging distributions of all keywords
Figure 6: Distributions after the second noise filtering
Figure 7: The result of topic modeling for the researcher from Figure 1
6. Comparing with categorized papers
Let us compare the topic modeling results based on keywords from researchers’ profiles in Google
Scholar and based on categorized publications of the researchers in Dimensions. For this we take
three researchers:
Ronald Yager with the interests Computational Intelligence, Fuzzy Logic, and Artificial
Intelligence;
Nataliia Kussul with the interests Machine Learning, Remote Sensing, Data Science, Disaster
Management, and Agricultural Monitoring;
Yevgeniy Bodyanskiy with the interests Computational Intelligence, Data Mining, Data
Stream Mining, and Big Data.
The results of topic modeling of researchers with the above-mentioned interests are presented in
Table 2.
These researchers have a considerable number of publications in Dimensions for the last 5 years
that allows getting statistically significant results.
During the analyzed period, Ronald Yager published 141 papers that are categorized by 20
research groups. The most publications – 63 are assigned to the research group H1. Yevgeniy
Bodyanskiy published 88 papers. They are categorized by 12 research groups. The most
publications – 59 are assigned to the research group H1. Nataliia Kussul published 47 papers that are
categorized by 14 research groups. The most publications – 21 are assigned to research group I9. By
using the third stage of the proposed algorithm on papers distributions, we get membership degrees to
research groups (Table 2).
Table 2
The results of researchers’ topic modeling
Research Ronald Yager Nataliia Kussul Yevgeniy Bodyanskiy
group Dimensions Google Dimensions Google Dimensions Google
Scholar Scholar Scholar
D6 0.283
I9 0.675 0.447
H1 0.63 0.441 0.172 0.346 0.797 0.295
H2 0.559 0.199
H6 0.37 0.153 0.203 0.506
Comparing the results, we see that topic modeling based on interests from Google Scholar –
laconic subjective information, with the proposed algorithm categorizes researchers good enough. For
quantitative assessment of the results, we used Czekanowski metric. For the case when membership
degrees are in [0, 1], Czekanowski metric between two researchers W1 and W2 is computed in the
following way:
Fit W1 ,W2
min( t p (W1 ), t p (W2 )) .
____
(1)
p 1, m
The metric (1) can be interpreted as a sum of membership degrees of the intersection of fuzzy sets
~ ~
W1 and W2 , that represent the topic modeling results based on two source of information – interests
from Google Scholar and categorized publications in Dimensions.
Based on the data from Table 2 we get the following assessments using metric (1):
Fit Yager 0.441 ;
Fit Kussul 0.619 ;
Fit Bodyanskiy 0.498 .
Using metric (1) the match is computed with isolate assumption – only in the scope of each
individual research group. To take into account the contribution of similar research groups we
propose to the value of metric (1) to add the following interactive addend:
Fit W1 ,W2
_____
J tv , t p min tv W1 , t p W2
_____
(2)
v 1, M p 1, M
where J t v , t p denotes Jaccard index between research groups tv and t p ;
W min 0, W W denotes residual of membership degree to research group t in
tv 1 tv 1 tv 2 v
~
W1 after taking into account the matching between tv W1 and t v W2 in (1);
t p W2 min 0, t p W2 t p W1 denotes residual of membership degree to research group t p in
~
W2 after taking into account the matching between t p W1 and t p W2 in (1).
To filter information noise, we use the formula (2) only for pairs of research groups with a high
level of similarity – with Jaccard index greater than 0.02. For the research groups from the Table 2 we
have 2 such pairs. Jaccard indexes for them are the following:
J D 6, I 9 0.083 ;
J H 1, H 6 0.071 .
Substituting the indexes in (2), we get:
Fit Yager 0 ;
Fit Kussul min 0.675 0.447,0.283 0.083 min 0.346 0.172,0.153 0.071 0.03 ;
Fit Bodyanskiy min 0.797 0.295,0.506 0.203 0.071 0.022 .
By taking into account the similarity of research groups the level of matching the topic modeling
results takes the following values:
Fit sim Yager 0.441 0 0.441 ;
Fit sim Kussul 0.619 0.03 0.649 ;
Fit sim Bodyanskiy 0.498 0.022 0.52 .
7. Conclusions
We proposed researchers’ topic modeling based on their interests in Google Scholar profiles.
Interests in profiles researchers specify based on their discretion without using any vocabulary of
keywords. In the paper, we propose an approach to categorization of such researchers using the
research classification system ANZSRC. A mapping “researcher – research groups” is done using
information system Dimensions that contains more than 110 millions of publications categorized
according to ANZSRC.
The algorithm of researchers’ topic modeling has 3 stages. The first stage forms a set of queries
based on keywords and their combination. On the second stage we perform topic modeling using each
query separately with filtering stop-words and underused words. On the third stage we average
membership degrees of all queries and cut the distribution to a few research groups. When dropping
minor research groups their contribution is redistributed to the leaders. As a result, we get
membership degrees for a researcher to a few research groups that correspond to the set of his
interests the most. Such mapping of interests can be viewed as an analog to the word2vec procedure.
We compared topic modeling based on small amount of information from researchers’ profiles at
Google Scholar with topic modeling based on a few dozens of authored publications categorized by
Dimensions. As a result of comparison, we get a good matching of topic modeling results based on
different sources of initial information. It allows using the proposed algorithm as the intellectual core
of information technology in regards to scientific staff, in particular, for the selection of candidates as
opponents of a dissertation, as reviewers for research projects, for forming a team to collaborate on
shared research projects etc.
8. References
[1]. A. Martín-Martín, M. Thelwall, E. Orduna-Malea, E.D. López-Cózar, Google Scholar, Microsoft
Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary
comparison of coverage via citations, Scientometrics 126 (2021) 871–906. doi: 10.1007/s11192-
020-03690-4.
[2]. A. W. Harzing, S. Alakangas, Google Scholar, Scopus and the Web of Science: A longitudinal
and cross-disciplinary comparison, Scientometrics 106(2) (2016) 787–804. doi: 10.1007/s11192-
015-1798-9.
[3]. B. Rahdari, P. Brusilovsky, D. Babichenko, E. B. Littleton, R. Patel, J. Fawcett, Z. Blum,
Grapevine: A profile-based exploratory search and recommendation system for finding research
advisors, Proceedings of the Association for Information Science and Technology 57(1) (2020).
doi: 10.1002/pra2.271.
[4]. J. Saad-Falcon, O. Shaikh, Z. J. Wang, A. P. Wright, S. Richardson, D. H. Chau, PeopleMap:
Visualization Tool for Mapping Out Researchers using Natural Language Processing, arXiv
preprint (2020), arXiv:2006.06105.
[5]. M. Rosen-Zvi, T. Griffiths, M. Steyvers, P. Smith, The author-topic model for authors and
documents, In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence,
AUAI Press (2004) 487-494.
[6]. D. Blei, A. Ng, M. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research 3
(2003) 993-1022.
[7]. J. Jian, G. Qian, M. Haikun, C. Chong, Author–Subject–Topic model for Reviewer
Recommendation, JIS-Journal of Information Science 4 (2019). doi:
10.1177/0165551518806116.
[8]. D. Mimno, A. McCallum, Expertise modeling for matching papers with reviewers, in:
Proceedings of the 13th ACMSIGKDD international conference on knowledge discovery and
data mining, KDD’ 07, ACM, San Jose, CA, 2007. doi: 10.1145/1281192.1281247.
[9]. C. Sun, T. J. King, P. Henville, R. Marchant, Hierarchical Word Mover Distance for
Collaboration Recommender System, Springer 996 (2018) 289-302. doi: 10.1007/978-981-13-
6661-1_23.
[10]. K. Xiangjie, J. Huizhen, Y. Zhuo, A. Tolba, X. Zhenzhen, X. Feng, Exploiting Publication
Contents and Collaboration Networks for Collaborator Recommendation, PlosOne 11(2):
e0148492 (2016). doi: 10.1371/journal.pone.0148492
[11]. Y. Zhao, J. Tang, Z. Du, EFCNN: A Restricted Convolutional Neural Network for Expert
Finding, volume 11440 of Lecture Notes in Computer Science, Springer, Cham, 2019. doi:
10.1007/978-3-030-16145-3_8.
[12]. A. Omer, G. Hongyu, B. Suma, H. Wen-Mei, X. JinJun, PaRe: A Paper Reviewer Matching
Approach Using a Common Topic Space, in: Proceedings of the 2019 Conference on Empirical
Methods in Natural Language Processing and the 9th International Joint Conference on Natural
Language Processing (EMNLPIJCNLP), Association for Computational Linguistics, Hong
Kong, 2019. doi: 10.18653/v1/D19-1049.
[13]. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean, Distributed representations of words and
phrases and their compositionality, Neural Information Processing Systems 2 (2013) 3111–3119.
[14]. S. Shtovba, M. Petrychko, Jaccard Index-Based Assessing the Similarity of Research Fields in
Dimensions, CEUR Workshop Proceedings 2533 (2019) 117-128.
[15]. S. Shtovba, M. Petrychko, An Informetric Assessment of Various Research Fields Interactions
on Base of Categorized Papers in Dimensions, CEUR Workshop Proceedings 2845 (2021) 159-
169.