=Paper=
{{Paper
|id=Vol-2871/paper11
|storemode=property
|title=Measuring Interdisciplinary Interactions Using Citation Analysis and Semantic Analysis
|pdfUrl=https://ceur-ws.org/Vol-2871/paper11.pdf
|volume=Vol-2871
|authors=Lu Huang,Xingxing Ni,Xiang Chen,Yi Zhang
|dblpUrl=https://dblp.org/rec/conf/iconference/HuangNCZ21
}}
==Measuring Interdisciplinary Interactions Using Citation Analysis and Semantic Analysis==
https://ceur-ws.org/Vol-2871/paper11.pdf
1st Workshop on AI + Informetrics - AII2021
Measuring interdisciplinary interactions using citation
analysis and semantic analysis
Lu Huang1, Xingxing Ni 1, Xiang Chen1, Yi Zhang2
1 School of Management and Economics, Beijing Institute of Technology, China
2
Faculty of Engineering and Information Technology, University of Technology Sydney,
Australia
bjchenxiang@hotmail.com
Abstract. Interdisciplinary interactions and integrations have become a major feature of the
current development of science and technology. How to measure the strength of interdisciplinary
interactions between two disciplines is a crucial issue. In our study, we propose a novel
measurement framework based on both citation analytics and semantic analytics, which
integrates three indicators - direct citation, bibliographic coupling and research content.
Especially, LDA model is incorporated with a word embedding model to create a semantic
solution that effectively constructing discipline-keyword vectors based on bibliometric data. At
last, entropy method is applied with these three indicators to assess the interdisciplinary
interactions strength. The interactions between information science & library science and other
six subjects are analyzed as the case study to demonstrate the reliability of the methodology, with
subsequent empirical validations.
Keywords: interdisciplinary interactions ··citation analysis ·semantic analysis ·word
embedding
1 Introduction
The importance of accelerating interdisciplinary interactions among disciplines is
increasingly recognized by people [1]. For example, 2017 Nobel Prize in chemistry
was awarded to physicists for solving biological problems. Cross combination of
information, methods, techniques, tools, perspectives, concepts and/or theories among
different disciplines or bodies of specialized knowledge has been promoted to form
interdisciplinary [2], which enables to advance fundamental understanding or to solve
problems whose solutions are beyond the scope of a single discipline or area of research
practice[3]. While interdisciplinary scientific research is increasingly concerned by
science and technology policy and management departments, people gradually began
to think about how to measure the strength of interdisciplinary interactions. Measuring
interdisciplinary interactions has been considered as a critical issue for the management
practice of interdisciplinary in scientific research management departments [4], which
Copyright 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
1
is conducive to evaluate the degree of interdisciplinary and grasp the current developing
situation of the discipline, and also optimize the disciplinary layout in the future [5].
Many studies have been devoted to how to measure the interdisciplinary nature of
basic research, i.e., citation analysis [6], co-author analysis [7], subject categories (SCs)
and journal disciplines [8]. As citation analysis could trace the cited literature and
identify the learning and referring relationships between disciplines, it has been widely
applied [9]. Complementary to these literatures, some researchers are from the
perspective of research content analysis, which is more microcosmic and specific and
can reveal the specific integration and diffusion process of knowledge, exploring the
development and change of disciplinary knowledge structure [10], for example, Xu et
al. have explored the interdisciplinary of the topics based on co-word analysis [2].
However, co-word analysis ignores terminological variations (e.g., “data mining” and
“data analytics”) and semantic relationships between terms from disciplines [11].
In this paper, we propose a novel framework of measuring the strength of
interdisciplinary interactions between two disciplines based on citation analysis and
semantic analysis from cognitive dimension. Especially, an LDA model is incorporated
with a word embedding model to construct discipline-keyword vectors, which could
explore the semantic and contextual relationships in order to capture their intersections.
We demonstrate our method via a case study of interdisciplinary interaction
measurements between “Information science & Library science” and other six
disciplines, i.e., “Education & Educational research”, “Computer science, Information
systems”, “Management”, “Economics”, “Mathematics, Applied”, and “Psychology,
Applied”.
2 Methodology
The framework of measuring interdisciplinary interactions is shown in Fig. 1.
2
Fig. 1. The framework of interdisciplinary interactions measurements
2.1 Data acquisition and preprocessing
The full records and references of research articles of two specific disciplines (Subject
Category) are acquired from the Web of Science (WoS) as the input, which include data
such as titles, abstracts, keywords and references. Then, we preprocess the data,
including the following work:
Subject classification matching of references.
Because the downloaded citation information is only the journal to which the citation
belongs, and there is no discipline (Subject Category) to which the journal belongs, we
need to use Python to obtain the Subject Category information of all journals on the
Journal Citation Reports (JCR) website of WoS. In addition, the Journal of the reference
in the citation information of some discipline downloaded from WoS uses the
abbreviation of the journal, so the full journal title should be obtained from JCR
database. Finally, we construct a comparison table of the abbreviation-full journal title-
Subject Category, and match the Subject Category of references according to this.
3
Term clumping.
A natural language processing (NLP) technique is applied to retrieve key terms from
the titles and abstracts, and a term clumping process removes noise, consolidates terms,
and identifies core terms [12]. We call the key terms after term clumping by keywords.
2.2 Citation analysis
The aim of this part is to measure the interdisciplinary degree between two disciplines
from the perspective of citation analysis. In this paper we provide two types of citation
analysis: one is direct citation, which reflects a two-way interactive relationship and the
most direct knowledge exchange between two disciplines [13]; the other is
bibliographic coupling, which reflects the situation that the two disciplines cite other
literatures together [4]. Jaccard similarity coefficient [14] is applied for both direct
citation and bibliographic coupling, and the higher the value is, the stronger the strength
of interdisciplinary interactions is.
Fig. 2(1). Subject classification structure of Fig. 2(2). Schematic diagram of reference coupling
references of Discipline X and Discipline Y calculation
Fig.2. Citation relationship between Discipline X and Discipline Y
Interdisciplinary interactions strength based on direct citation.
The subject classification structure of references of Discipline X and Discipline Y is
𝑑𝑐
shown in Fig.2 (1). Here, we denote 𝐼𝐼𝑥𝑦 as the strength of interdisciplinary
interactions between Discipline X and Discipline Y based on direct citation. The direct
citation relationship between discipline X and Y focus on two sets (diagonal line
sections): the references of Discipline X belonged to Discipline Y, and the references
of Discipline Y belonged to Discipline X. Following Jaccard’s calculation formula, the
numerator is the intersection of the above two parts, which is the minimum reference
number of the two sets; while the denominator is the sum number of references
𝑑𝑐
belonged to other disciplines (shadow sections) minus the numerator. Therefore, 𝐼𝐼𝑥𝑦
can be represented as:
𝑑𝑐 min{𝑗𝑥𝑦,𝑗𝑦𝑥 }
𝐼𝐼𝑥𝑦 = (1)
𝑘𝑥 +𝑘𝑦 −min{𝑗𝑥𝑦 ,𝑗𝑦𝑥 }
4
Where 𝑗𝑥𝑦 is the number of references of Discipline X belonged to Discipline Y,
𝑗𝑦𝑥 is the number of references of Discipline Y belonged to Discipline X, 𝑘𝑥
represents the number of references of Discipline X belonged to disciplines other than
Discipline X,𝑘𝑦 represents the number of references of Discipline Y belonged to
disciplines other than Discipline Y.
Interdisciplinary interactions strength based on bibliographic coupling.
As shown in Fig.2 (2), bibliographic coupling relationship between Discipline X and
𝑏𝑐
Discipline Y focus on the common references. Here, we denote 𝐼𝐼𝑥𝑦 as the strength of
interdisciplinary interactions between Discipline X and Discipline Y based on
bibliographic coupling. Following Jaccard’s calculation formula, the numerator is the
number of common references of Discipline X and Discipline Y, and the denominator
is the number of references union of Discipline X and Discipline Y. Therefore, and
𝑏𝑐
𝐼𝐼𝑥𝑦 can be represented as:
𝑏𝑐 𝑜𝑥𝑦
𝐼𝐼𝑥𝑦 = (2)
𝑞𝑥 +𝑞𝑦 −𝑜𝑥𝑦
Where 𝑞𝑥 is the number of references of Discipline X, 𝑞𝑦 is the number of references
of Discipline Y, 𝑜𝑥𝑦 represents the number of common references of Discipline X and
Discipline Y.
𝑑𝑐 𝑏𝑐
In this part, we finally generate two indicators: 𝐼𝐼𝑥𝑦 and 𝐼𝐼𝑥𝑦 .
2.3 Semantic analysis
The purpose of this part is to measure the interdisciplinary interactions strength between
two disciplines by exploring semantic relationship, which is reflected by the keywords
[15]. With the development of disciplines, due to the interaction between disciplines,
the overlapping of disciplines can be expected in some areas of knowledge [4].
Therefore, discipline-keyword vectors are constructed to calculate the similarity of
research content of disciplines.
First, Latent Dirichlet Allocation (LDA) model, which is a probabilistic topic model
and defines a global hierarchical relationship from words to a topic and then from topics
to a document [16], is applied to obtain keyword distribution of both Discipline X and
Discipline Y. Specifically, we synthesize the keywords which generated by cleaning
the paper data (titles and abstracts) of Discipline X and Discipline Y to their own
keyword documents. Through LDA model, discipline documents are represented as
topic probability distribution, and topics are represented as keyword probability
distribution. Then, the discipline-keyword matrix could be obtained by multiplying
discipline-topic matrix and topic-keyword matrix. We denote A (m, p) as the discipline-
keyword matrix of Discipline m for keyword p.
Second, Word2Vec model is used to generate keyword vectors. Word2vec is a word
embedding model to represent keywords as word vectors, which could capture context
semantic information [17]. In our study, skip-gram modules is applied, since it has
proven to have a tiny advantage with bibliometric data [18]. The inputs are word
sequences generated from the text in the abstracts and titles. Keywords of Discipline X
and Discipline Y are then mapped as vectors originating from a point in a multi-
dimensional semantic space.
5
Then, the research content of disciplines could be converted into vector
representation by loading the keyword vectors created in previous step into matrix A
(m, p), and we denote 𝑉𝑚𝑝 as a discipline-keyword vector. It can be represented as:
𝑉𝑚𝑝 = ∑ A(𝑚, 𝑝) ∗ 𝑉𝑝 (3)
where 𝑉𝑝 denotes the vector of keyword p.
Lastly, the similarity between discipline-keyword vectors of disciplines is calculated
𝑟𝑐
according to the Euclidean distance. We denote 𝐼𝐼𝑥𝑦 as the strength of
interdisciplinary interactions between Discipline X and Discipline Y based on research
content, and it can be represented as:
𝑟𝑐 1
𝐼𝐼𝑥𝑦 = (4)
𝜌𝑥𝑦
where 𝜌𝑥𝑦 is the Euclidean distance between the vectors of Discipline X and
Discipline Y.
𝑟𝑐
In this part, we finally generate indicator 𝐼𝐼𝑥𝑦 .
2.4 Multi-index Synthesis
𝑑𝑐 𝑏𝑐 𝑟𝑐
At this step, three indicators –𝐼𝐼𝑥𝑦 ,𝐼𝐼𝑥𝑦 , 𝐼𝐼𝑥𝑦 are standardize with the Z-score method.
In order to integrate the strength of interdisciplinary interactions of the three aspects
more reasonably, it is necessary to set the weight of each index. There are two ways to
determine the index weight: subjective weight and objective weight. We use the
objective weighting method, because it can overcome the randomness of subjective
weighting, and more objectively represent the importance of the weight. By comparing
various objective weighting methods, we decided to use entropy weight method to
calculate.
Entropy weight method is an objective method to determine the index weight based
on mathematical statistics and the basic principle of information theory [19]. It can
effectively consider the variation degree of indicators of the strength of
interdisciplinary interactions. In this paper, the entropy weight of each index is
defined as Wβ. The calculation method is shown in formula (5), (6) and (7).
𝑞𝛼𝛽
𝑓𝛼𝛽 = ∑ (5)
𝛼 𝑞𝛼𝛽
Where, 𝑓𝛼𝛽 is the characteristic specific gravity of the index, 𝑞𝛼𝛽 is the value
of each indicator.
∑𝛼 𝑓𝛼𝛽ln(𝑓𝛼𝛽)
𝐸𝛽 = (6)
ln(𝑁)
Where, 𝐸𝛽 is called information entropy. N is the number of indicators. If the
information entropy of an index is smaller, it means that the variation degree of the
index value is greater, the amount of information covered is more, and its influence
ability in the overall evaluation is greater, so it has a greater weight.
1−𝐸𝛽
𝑊𝛽 = (7)
𝑀−∑𝛽 𝐸𝛽
6
The comprehensive strength of interdisciplinary interactions between Discipline X
and Discipline Y 𝐼𝐼𝑥𝑦 could be calculated as:
𝑑𝑐 𝑏𝑐 𝑟𝑐
𝐼𝐼𝑥𝑦 =W1*𝐼𝐼𝑥𝑦 + 𝑊2 𝐼𝐼𝑥𝑦 + 𝑊3 𝐼𝐼𝑥𝑦 (8)
Where, W1，𝑊2 ，𝑊3 are the weights of the three indicators which calculate by the
entropy method [19].
3 Case study
We chose Information science & Library science (LIS) as the major discipline and other
six disciplines to test our framework, i.e., “Education & Educational research”,
“Computer science, Information systems”, “Management”, “Economics”,
“Mathematics, Applied”, and “Psychology, Applied”. Because LIS combines basic
research, like mathematics, computer, and physics, with the real-world needs of social
sciences.
3.1 Data acquisition and preprocessing
The research papers and references of seven disciplines from Web of Science (WOS)
in the year of 2019 are the data in this study. Search strategies include
“WC=Information Science & Library Science”, “WC=Education & Educational
Research”, “WC=Computer Science, Information Systems”, “WC=Management”,
“WC=Economics”, “WC=Mathematics, Applied”, “WC=Psychology, Applied”. The
SCI-EXPANDED, SSCI in the Web of Science selects and makes use of subject
categories in the Web of Science via these search strategies, which selected the article
type to retrieve the articles in English. We retrieved 127235 papers and 1505717
references in total (Table 1). We download the full records and refences of each
discipline.
Table 1. Number of papers and references of seven disciplines
Subject Category Papers References
Information science & Library science (LIS) 4423 75908
Education &Educational Research (Edu) 15590 167748
Computer Science, Information Systems (Com) 35308 369168
Management (Mag) 13941 231237
Economics (Eco) 24158 279902
Mathematics, Applied (Mat) 29042 274609
Psychology, Applied (Psy) 4773 107145
Total 127235 1505717
Then, We use Python to download the journals provided by JCR and their subject
category information to construct the journal-Subject Category comparison table.
Then, using the full journal title obtained from JCR database by python, the
abbreviation-full journal title comparison table is established. Finally, the
abbreviation-full journal title -Subject Category comparison table is obtained,
including 11375 journals included in WoS, and finally 17961 journal subject category
7
mapping results are obtained, that is, an average journal corresponds to 1.58 subject
categories. The partial results is shown as Table 2.
Table 2. the abbreviation-full journal title -Subject Category comparison table (Partial)
the abbreviation full journal title Subject Category
INTERNATIONAL JOURNAL OF Information science
INT J INFORM MANAGE
INFORMATION MANAGEMENT & Library science
Education
EDUCATIONAL
EDUC PSYCHOL &Educational
PSYCHOLOGIST
Research
Computer Science,
COMPUT NETW Computer Networks
Information Systems
ACAD MANAG ANN Academy of Management Annals Management
QUARTERLY JOURNAL OF
Q J ECON Economics
ECONOMICS
APPLIED MATHEMATICS Mathematics,
APPL MATH LETT
LETTERS Applied
J OCCUP HEALTH Journal of Occupational Health
Psychology, Applied
PSYCH Psychology
The NLP process retrieved 13186 terms from the titles and abstracts of the papers.
After term clumping [12], 12298 distinct terms remained.
3.2 Interdisciplinary interactions measurement based on citation analysis
Table 3 shows the specific data of direct citation and bibliographic coupling of six
disciplines with Information Science & Library Science. Follow the design in Section
2.2, the strength of interdisciplinary interactions based on both direct citation and
bibliographic coupling could be generated in Table 4.
Table 3. Citation relationship of six disciplines with Information Science & Library Science
Subject Category Direct Citation Bibliographic Coupling
Education &Educational Research
2190 13467
(Edu)
Computer Science, Information
4784 47752
Systems (Com)
Management (Mag) 5273 38897
Economics (Eco) 1581 14926
Mathematics, Applied (Mat) 219 1502
Psychology, Applied (Psy) 738 11041
Total 14785 127585
Table 4. Strength of interdisciplinary interactions between six disciplines and LIS
Based on direct citation Based on bibliographic coupling
Subject Category
(%) (%)
8
Education &Educational Research (Edu) 0.9070 5.8504
Computer Science, Information
1.0866 12.0184
Systems (Com)
Management (Mag) 1.7468 14.5004
Economics (Eco) 0.4463 4.3786
Mathematics, Applied (Mat) 0.0625 0.4304
Psychology, Applied (Psy) 0.4048 6.4187
3.3 Interdisciplinary interactions measurement based on research content
Follow Section 2.3, discipline-keyword matrix of each discipline was generated by
LDA model, which includes 7 subjects and 12298 keywords. Then, Word2vec model
was applied to map keywords into dense word vectors to capture semantic information
of keywords. Since higher dimensions have been shown to capture better semantics
[20], we set the number of dimensions for the vectors to 450, and the keywords of 7
disciplines were converted into semantic-level vectors by the trained model.
Furthermore, discipline-keyword vector could be generated following formula (3).
According to Euclidean distance, we could finally generate the interdisciplinary
interactions strength between six disciplines and Information Science & Library
Science, and the results are shown in Table 5.
Table 5. Strength of interdisciplinary interactions between six disciplines and Information
Science & Library Science
Subject Category Based on research content （%）
Education &Educational Research (Edu) 28.3940
Computer Science, Information Systems (Com) 19.8481
Management (Mag) 21.7641
Economics (Eco) 16.5775
Mathematics, Applied (Mat) 16.9179
Psychology, Applied (Psy) 14.7513
Z-score method was used to standardize three indicators, and entropy method was
applied to calculate the index weight. Finally, the strength of interdisciplinary
interactions between six disciplines and Information Science & Library Science was
obtained, as shown in Fig. 3.
9
Fig. 3. Final results of the strength of interdisciplinary interactions between six disciplines
and LIS
There are some observations based on above analysis results:
1) The Strengths of interdisciplinary interactions between LIS and other six
disciplines are between 0.4335% and 14.4674%. It shows that the diversity of
knowledge sources of LIS is not high, which is consistent with the conclusions of Shao
et al. [21].
2）Among six disciplines, LIS focus more on similar disciplines for interdisciplinary
interactions with Computer science & information system and Management. Li et al.
proved that the speed of knowledge diffusion between LIS and Management shows a
continuous growth trend [22]; Shi et al. stated that both LIS and Computer Science,
Information Systems involve information science, especially in system design,
technology research, and algorithm optimization [23].
3) The strength of interdisciplinary interactions of LIS and Mathematics, Applied is
very low. Because Mathematics, Applied is a very professional discipline, while LIS
only uses mathematical knowledge in scientific metrology, information retrieval and
other research branches.
3.4 Validation
We conducted validation to prove the accuracy of our model: the comparison with the
mainstream interdisciplinary index. The main indicators include Salton coefficient [24],
Rao-Striling coefficient [25] and ID value [3]. The results are shown in Table 6.
Table 6. Comparison of the results between our method and mainstream indicators
Edu Com Mag Eco Mat Psy
Value #Rank Value #Rank Value #Rank Value #Rank Value #Rank Value #Rank
Our method 5.8426% #4 11.9905% #2 14.4674% #1 4.3709% #5 0.4335% #6 6.4044% #3
Salton 11.9343% #4 28.5257% #2 29.3592% #1 10.2399% #5 1.0403% #6 12.2427% #3
Rao-striling 0.4215% #3 0.4376% #2 0.5221% #1 0.0032% #5 0.0021% #6 0.0042% #4
10
ID 3134 #4 121 #2 54 #1 10043 #5 103092 #6 2037 #3
It can be seen that: 1) The ranking of strength of interdisciplinary interactions
calculated by our method is similar to that of other mainstream methods, which proves
the effectiveness of this method. 2) There is a large gap between the minimum and
maximum of the strength of interdisciplinary interactions calculated by other
mainstream indicators. For example, in the calculation results of Salton coefficient, the
maximum value is 35.8766%, and the minimum value is only 1.1561%. The strength
of interdisciplinary interactions between LIS and Computer Science, Information
Systems is too high and Inconsistent with the actual situation. And the ID value is too
big to understand. 3) The difference between the results calculated by other mainstream
indicators is not obvious. For example, in the Rao-Striling calculation results, the
strength of interdisciplinary interactions between LIS and Edu is 0.4215%, and is
0.4376% between IS and Com. The strength of intersection between LIS and these two
subjects are too close. It shows the superiority of our method.
Therefore, compared with other models, the model proposed in this paper is more
realistic, and distinguishable, which performs well.
4 Conclusion
In this paper, we propose a measurement model of interdisciplinary interactions
strength between two specific disciplines, which takes the reference relations between
disciplines and the semantic relations of research contents into account. For semantic
analysis, the combination of word2vec and LDA can build a more multi-dimensional
discipline- keyword vectors, which could accurately explore the similarity of research
content between two disciplines.
We believe our method which integrating semantic analysis into citation analysis not
only shows a fresh perspective and thought for measuring interdisciplinary interactions,
but also other quantitative bibliometric problems. In addition, the method could be
applied to disclose the dynamics of interdisciplinary research on a larger sample of
disciplines.
Several future directions of research would address the limitations of this study. First,
we only selected the data from Web of Science database in the year of 2019, which may
not truly reflect the relations of two disciplines based on one year data. Second, this
paper only considers the simple citation relationship of interdisciplinary references,
ignoring the relevance of citation content.
In future research, we can combine text analysis method with citation content to
explore a deeper interdisciplinary relationship.
Acknowledgements
This work was supported by the National Science Foundation of China Funds [Grant
No. 71774013] and the Australian Research Council under Discovery Early Career
Researcher Award DE190100994.
11
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