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PAPER

Special Section on Data Engineering and Information Management

Topic Representation of Researchers’ Interests in a Large-Scale
Academic Database and Its Application to Author Disambiguation
Marie KATSURAI†a) , Member, Ikki OHMUKAI††b) , Nonmember, and Hideaki TAKEDA††c) , Member

SUMMARY
It is crucial to promote interdisciplinary research and recommend collaborators from diﬀerent research fields via academic database
analysis. This paper addresses a problem to characterize researchers’ interests with a set of diverse research topics found in a large-scale academic
database. Specifically, we first use latent Dirichlet allocation to extract topics as distributions over words from a training dataset. Then, we convert
the textual features of a researcher’s publications to topic vectors, and calculate the centroid of these vectors to summarize the researcher’s interest
as a single vector. In experiments conducted on CiNii Articles, which is
the largest academic database in Japan, we show that the extracted topics
reflect the diversity of the research fields in the database. The experimentresults also indicate the applicability of the proposed topic representation
to the author disambiguation problem.
key words: researcher analysis, academic database, topic model, author
disambiguation

1.

Introduction

Knowledge acquisition from academic papers has received
much attention since huge digital libraries have become
available as a result of database systems and Internet development. Various approaches have been presented tomap and visualize the structures of scientific research activities at diﬀerent levels of granularity including academic
papers [1], [2], conferences [3], and researchers [4]–[7]. In
particular, analyzing researcher relationships is useful formeasuring the influence of authors [4], discovering communities [6], and recommending research collaborators [7].
Conventional methods of academic database analysis
can be roughly divided into two approaches on the basis of the features they exploit: the bibliographic metadata and textual content of papers. The bibliographic metadata approach uses author lists or citations to derive coauthorship networks [8] or co-citation networks [8]–[10], towhich network analysis methods are applied in order to capture the scientific structure of a target database. In contrast, the recently-proposed textual content approach, uses
the words of academic papers to characterize conferences or
researchers [3], [5]. One advantage of such a content-based
Manuscript received July 2, 2015.
Manuscript revised November 10, 2015.
Manuscript publicized January 14, 2016.
†
The author is with Doshisha University, Kyotanabe-shi, 610–
0394 Japan.
††
The authors are with the National Institute of Informatics, Tokyo, 101–8430 Japan.
a) E-mail: katsurai@mm.doshisha.ac.jpb) E-mail: i2k@nii.ac.jpc) E-mail: takeda@nii.ac.jp
DOI: 10.1587/transinf.2015DAP0031

analysis is that it can determine the potential relevance of
papers, which cannot be identified by the metadata-based
approach. However, these conventional methods have been
applied to a limited research domain (e.g., information retrieval and natural language processing), and their usefulness in a large-scale academic database has not been adequately demonstrated. Characterizing researchers’ interests
with a set of diverse topics is necessary to develop applications that are useful in an academic environment, for example, promoting interdisciplinary research and recommending collaborators from diﬀerent research fields.
This paper presents a topic representation of researchers’ interests in a large-scale academic database covering all research fields. The proposed method focuses on
the textual content of papers to characterize research fields.
Specifically, we use the abstract of each paper, which is generally the longest piece of text that is available from a single
paper in large-scale academic databases. In the proposed
method, latent Dirichlet allocation (LDA) [11] is applied toa collection of abstracts, and this process results in word
distributions for each research topic. With the use of thelearned LDA, each abstract in a target researcher’s publications is represented by a topic distribution. All topic distributions are finally summarized as a single vector to represent the researcher’s interests. We conduct experiments
using a dataset from CiNii Articles∗ , the largest academic
database in Japan, in order to validate the eﬀectiveness of
our approach.
The derived topic representations of researchers are potentially eﬀective for several applications. For example, we
use the topic vectors of the researchers for the author disambiguation task, i.e., choosing a correct author for a given
paper from researchers who share the same full name [12]–[15]. An added merit of this particular application is thatit can be evaluated quantitatively. The experiment results
show that the presented topic representation of researchers’interests achieves better author disambiguation performance
than comparative methods that directly use the textual features from abstracts or that are based on metadata.
In summary, the main contributions of this study are
twofold. First, to the best of our knowledge, this work is
the first to represent researchers’ interests in a large-scaleacademic database that includes not a specific, but a diverse range of research fields in Japan. Second, we show
the applicability of the topic representation of researchers∗

http://ci.nii.ac.jp/enc 2016 The Institute of Electronics, Information and Communication Engineers
Copyright KATSURAI et al.: TOPIC REPRESENTATION OF RESEARCHERS’ INTERESTS IN A LARGE-SCALE ACADEMIC DATABASE

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to author disambiguation. The remainder of this paper isorganized as follows. Section 2 provides a brief review
of related studies. Section 3 presents a topic model-based
approach that represents researchers’ interests with the useof the textual features of academic papers. Section 4 describes an application of the proposed method to the author
disambiguation problem. Section 5 presents the results ofthe experiments to verify the eﬀectiveness of the proposed
method. Finally, Sect. 6 gives a summary and some possible
directions for future work.
2.

Related Work

2.1

Knowledge Discovery from Academic Databases

Knowledge discovery from academic databases has along history in bibliometrics research, with most conventional methods using the bibliographic metadata of papers.
Small [1] assumed that more frequently co-cited papers are
more related and analyzed paper co-citation relationships.
This idea was extended to author analysis by White andGriﬃth [9]; they counted the frequency with which any paper of an author is co-cited with another author in the references of citing papers. Börner et al. [8] constructed a
co-authorship network, in which each node corresponds toa researcher, and each edge represents the number of coauthored papers between the researchers. Ding et al. [4] constructed an author co-citation network among the 108 mosthighly cited authors in the information retrieval field, andcalculated the ranks of the authors on the basis of PageRankmeasures. Radev et al. [10] defined network analysis-based
measures to rank authors or papers, and they conducted experiments using a collection of papers published in conferences of natural language processing (NLP).
On the other hand, recently presented methods do not
rely on bibliographic metadata but use the textual content
of papers. In these methods, topic models have played an
important role to describe the content of papers in a lowdimensional feature space. Hall et al. [3] used LDA to calculate the topic distributions of three international conferences
related to NLP, and they calculated the similarity between
the topic distributions of the conferences. In addition, by
calculating the entropy of the topic distribution, the authors
quantified the range of research themes that each conference
covers. The Hall’s method has been eﬀectively applied toanalyze other research fields such as software systems [16].
Lu and Wolfram [5] presented a topic model-based approach
to measure the relatedness between researchers. Specifically, they applied the author topic model (ATM) [17], an
extension of LDA with author information, to represent eachauthor by a multinomial distribution over topics. However,
the analysis in [5] is limited to only 50 researchers, andtheir research field involved is information science only.
Compared with the work in [5], the present study aims tocharacterize researchers’ interests in a diverse range of research topics, and it shows the usefulness of such an approach in facilitating the management of large-scale aca-

demic databases.
2.2 Author Disambiguation in Academic Databases
Author name ambiguity is a well-known and important issue to overcome in an academic database that oﬀers a paper search service [18]. Clustering [12], [13] and identifierassignment [14], [15] are two approaches for author disambiguation. Some digital libraries have introduced the clustering approach in practice to improve the eﬃciency of author search [19]–[21]. However, the authors in [22] reported
that the existing systems have yet to achieve a highly accurate identification.
Author identifier assignment is another name disambiguation approach, which provides direct links between
academic papers and a list of researchers. Thomson
Reuters provides this type of systems, which is called ResearcherID† , and the Open Researcher and Contributor ID(ORCID) initiative [23] attempts to assign global identifiers
to authors with the aim of linking several publishers. CiNii
Articles, whose overview is described in the Appendix, isalso equipped with an author identifier assignment system.
These identifiers can oﬀer precise information about an arbitrary author’s research activity in a database, so the evaluation of a researcher’s performance is possible. However,current systems require manual operation by the users themselves or by the database managers. The systems shouldpresent to users the results of classifying an author of a given
paper into one of the candidate researchers who share thesame full name to reduce the time for manual operation.
Thus, in this paper, we quantitatively show that our topic
representation of researchers’ interests is applicable to the
distinguishment of a correct author.
3.

Topic Representation of Researchers’ Interests in a
Large-Scale Academic Database

This section presents a topic model-based approach for representing researcher’s interests in a large-scale academic
database. An overview of the proposed method is shown in
Fig. 1. Given a training dataset, we first extract the textual
features of each paper. Then, we extract the topics by calculating the distributions over words with the use of LDA (seeSect. 3.1). This step enables the characterization of each paper with a set of topics. All publications of the researcher
are converted to topic vectors, and the centroid of these vectors is calculated to model a target researchers’ interests (seeSect. 3.2).
3.1 Topic Extraction from an Academic Database
This subsection describes how to extract topics from a target
academic database. Suppose that we are given a training
dataset consisting of N abstracts from the database, which isdenoted by D. For each abstract d ∈ D, we extract a vector†

http://www.researcherid.com/

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3.2 Characterization of Researchers with the Use of Topics

Fig. 1 The proposed steps to represent researchers’ interest: (a) topic
extraction through LDA training and (b) topic summarization from the researchers’ publications.

This section presents an approach to characterize researchers’ interests in a topic space calculated by LDA. Let
Ωr be a set of abstracts authored by a target researcher r.
From each abstract d ∈ Ωr , its word vector xd can be converted to a K-dimensional topic distribution θd by an inference in LDA. Our aim is to discover the set of topics expressed by the papers in Ωr and use these to characterize theresearcher r. Several possible ways can be used to summarize a set of vectors {θd |d ∈ Ωr }. In this paper, we calculate acentroid of the vectors by using a simple average approachdescribed as follows:
1 
mr =
θd ,
(1)
|Ωr | d∈Ωrof words, xd = [xd1 , xd2 , . . . , xdWd ], where Wd is the numberof words in abstract d. Let W be the number of unique words
in the training dataset, and V = [w1 , w2 , . . . , wW ] represents
a vocabulary of words.
The words that appear in an academic paper usually reflect a particular set of topics the paper deals with. We use
LDA to determine the set of topics that are used in the training dataset. With the assumption that K topics are foundin the database, LDA regards an abstract as a probabilistic
mixture of these topics. The generative process of LDA canbe formalized as follows [11]:• For each topic z ∈ {1, 2, . . . , K}, draw a W-dimensional
multinomial distribution φz from a Dirichlet distribution with prior β;• For each document d ∈ D, draw a K-dimensional
multinomial distribution θd from a Dirichlet distribution with prior α;• For each word xdi in document d, draw a topic zdi ∈{1, 2, · · · , K} from the multinomial distribution θd ;• Draw a word xdi from the multinomial distribution φzdi .
After the model training, the resulting distribution φz shows
which words are important in topic z, whereas the distribution θd represents which topics are included in paper d. To
calculate these distributions, we exploit the collapsed Gibbs
sampling [24]. Given a new abstract, we can estimate its
Kin
topic distribution by using the fixed distribution {φz }z=1
the sampling framework. The calculation of the topic distribution corresponds to reducing the dimensionality of the
textual feature space from W to K, which can deal with thevariability in word choice across research fields in a largescale academic database.
Unlike ATM used in [5], LDA itself does not provide
information about the interests of authors during training.
However, we solve that using a two-stage approach that firstestimates a topic proportion of a given paper; and then uses
it for calculating the researchers’ interests. The detail of thisapproach is described in the following subsection.

where |Ωr | denotes the number of abstracts in Ωr . The resulting vector encodes the information about research topics
that the papers focus on. Such a vector representation ofresearchers’ interest has several potential applications. For
example, to calculate the relatedness between researchers r1
and r2 , we can exploit the similarity between their topic vectors mr1 and mr2 .
The proposed method can be applied to an arbitrary
academic database if a set of papers is accurately linked
to researchers in the database. As mentioned in Sect. 2.2,
current academic databases usually require the manual author identifier assignment because some researchers whoshare the same full name: this requirement entails timeconsuming work. Thus, we try to use the proposed method
to solve an author disambiguation problem in the following
section.
4.

Author Disambiguation Using Topic Representationof Researchers’ Interests

This section describes how to use the proposed topic representation of researchers’ interests as one of its possible applications. We assume that a target database has researcher
identifiers. We consider the following situation: when a
new abstract dnew is given to the database, more than one researcher has the same name as that of an author of dnew . The
task is to assign the correct researcher identifier to the author. Let us denote a set of researchers whose full names are
a by Ra . To disambiguate the author name a in dnew , we use aframework illustrated in Fig. 2. First, words from the paper
dnew are converted to a topic distribution θdnew . Then, using
the topic vectors of the candidate researchers {mr |r ∈ Ra },we select the correct author as follows:r∗ = arg min D(mr , θdnew ),
r∈Ra

(2)

where D(mr , θd ) is a distance measure between two vectors mr and θdnew . By comparing the topic vector of the paper with the topic vector of each researcher, we can disambiguate its author name.

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Table 1

Details of the training dataset used in the experiments.
Number of papers
Number of researchers
Number of unique words
Average number of words per a paper

104,705
31,39985,58949

5.1 Dataset

Fig. 2 An illustration of the application of the researcher’s topic
representation to author disambiguation.

Presenting the result of author disambiguation to users
can reduce the manual eﬀort in maintaining the database.
After the identifier of paper dnew is fixed, we update the topic
representation of the researcher r∗ as follows:


1
mr∗ ←
(3)
θdnew + |Ωr∗ |mr∗ ,1 + |Ωr∗ |Ωr∗ ← {dnew ∪ Ωr∗ }.
(4)With the use of these equations, the topics of the given abstract can be easily reflected in the latest topic vector of
the researcher. This update method is also a contributionof our method to manage the large-scale academic database
compared with the most related work [5]: the conventional
method [5] assumes that the target academic database isstatic over time, and it does not show any scheme to update
the researcher’s information based on his/her new publications. Thus, if a new paper is added to the database, themethod in [5] requires to be trained again using a wholedataset, which is very time-consuming and intractable ina large-scale academic database. On the other hand, ourmethod assumes that the target database is populated withnew papers and can eﬃciently update a researcher’s topic
vector using Eqs. (3) and (4) when given his/her new publications.
5.

Experiments

In this section, we present experimental results that verify the eﬀectiveness of the proposed method. The detailsof the datasets used in our experiments are described in
Sect. 5.1. Then, the results of topic extraction from thedataset, which represent some researchers’ interests, are presented in Sect. 5.2. Finally, an application of the topic representation of researchers’ interests to author disambiguation
is evaluated in Sect. 5.3.

This subsection explains how we constructed a dataset basedon CiNii Articles. First, to cover active researchers in all research fields in Japan, we extracted a set of researcher identifiers from the research grant awards database KAKEN† .
The relationship between CiNii Articles and KAKEN is
described in the Appendix. Then, all abstracts written in
Japanese by each researcher were retrieved from CiNii Articles: a total of more than 220K papers were identified. By
randomly sampling the papers so that the total number of
each researcher’s abstracts is less than 15, we constructed a
training set of 104,705 papers. To extract a set of words fromeach abstract in the training set, we performed morphological analysis using MeCab†† . Specifically, by introducing alist of page names in Wikipedia††† to MeCab, we regarded
named entities as nouns (e.g., “support vector machine” wasused as a proper noun). Finally, unigrams and bigrams ofconsecutive nouns were used as the textual features of theabstract. To reduce the eﬀects of noisy words for training
LDA, we discarded words that appeared in more than 5% of
the total number of papers or words that appeared in fewer
than five papers: a vocabulary that consists of 85,589 words
was derived. The details of the training dataset constructed
are shown in Table 1.
5.2 Results of Topic Extraction and Representing of Researchers’ Interests
In this subsection, we first present the results of topic extraction, using the training dataset from CiNii Articles. Basedon the training dataset, we calculated the topic distributions
over words using LDA, as described in Sect. 3.1, in which
the number of topics was empirically set as K = 500. The
LDA hyperparameters α and β were set to 50/K and 0.01,
respectively. Note that these model parameters can be automatically selected based on the training dataset using sophisticated methods [25], [26], which will be investigatedin future work. Table 2 shows a few examples of the extracted topics†††† . The table shows that the extracted topics
span a diverse range of research fields, including sociology,
programming, architecture, and clinical medicine. We canalso see that the extracted topics were at diﬀerent granularities: for example, Topic 358 corresponds to a specificresearch theme (optical communication), while Topic 432†

https://kaken.nii.ac.jp/en/
https://code.google.com/p/mecab/†††
https://www.wikipedia.org/††††
Originally, all the topics obtained by the method were explained by Japanese words.
††

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Table 2 Examples of topics extracted from the training dataset. Our interpretations of the topics arein bold text.
Topic 22
Topic 25
Topic 47
Topic 172
sociology
programming
histochemistry
rice cropping
Word/Phrase
Prob.
Word/Phrase
Prob.
Word/Phrase
Prob.
Word/PhraseProb.
flooding
0.0377social0.1016
program
0.0457
immune tissue0.0323society
0.0355
object0.0420histochemistry0.0315weedkiller
0.0274
cultural
0.0240description
0.0348chemical
0.0269plowing
0.0228sense of values
0.0216
object-oriented
0.0328squamous epithelium
0.0268cc
0.0206
community
0.0120processing system
0.0287cancer cell0.0225
paddy field
0.0201
opinion0.0118
implementation
0.0277
expression
0.0214yield
0.0178Topic 260
circuits
Word/Phrase
power consumption
circuitdesignfpga
logic circuit
reduction

Prob.
0.06980.03320.01700.01610.01560.0130Topic 273
cropping
Word/Phrase
yieldripening
leaf area
dry weights
heading timenumber of ears

Topic 358
optical communication
Word/PhraseProb.
waveguide
0.0535refractive index
0.0294
optical fiber
0.0195resonator
0.0178light wave
0.0128light
0.0099Topic 404
architecture
Word/Phraseindoorthermal environment
comfortability
skin temperature
heat loadindoor environment

Topic 432
image processing
Word/Phrase
Prob.
image
0.1614
original image
0.0225
input image
0.0201
contour
0.0183
photography
0.0171
pixel
0.0143Topic 458
meteorology
Word/Phrase
precipitationwind
observation
atmospheredevelopcloud

Topic 277
botanyProb.
0.02400.02340.02110.01680.01560.0133Prob.
0.03790.03540.03110.02870.01990.0176Prob.
0.07570.01800.01280.12300.01080.0992

Topic 415
clinical medicine
Word/Phrase
Prob.
tumor
0.0580diagnosis
0.0334lump
0.0248case
0.0236
enforcement
0.0187
histopathology
0.0142Prob.
0.09800.03290.02350.02170.02160.0205Topic 479
histology
Word/Phrase
histology
histopathology
histologicalmacroscopic
tissue image
fibrillation

Topic 405
biomassProb.
0.02060.01690.01660.01660.01150.0114

Word/Phrase
soilbiomass
andosolporemineralization
layer

Prob.
0.04220.01530.01260.00990.00930.0078Topic 463
fast algorithm
Word/Phrase
fastparallelization
execution timeprocessor
execution
performance improvement(image processing) appears in several research fields, including medical image analysis and video coding.
Moreover, we show how researchers were represented
with combinations of multiple topics by the proposed
method. Based on a list of research fields designed in
KAKEN, we randomly picked three research fields: computer system and networks, plant nutrition and soil science,and fundamentals of veterinary medicine, Then, we randomly chose five researchers registered with KAKEN fromeach research field, respectively, and we calculated their
topic vectors using Eq. (1). Figure 3 shows a heat mapof the topic representations of the fifteen researchers: thedarker cells indicate a higher assignment of the corresponding topic† . From this figure, we can see that researchers
from the same research field tend to share the same topics.
This research field-based analysis shows the eﬀectiveness ofthe topic vectors in representing researchers’ interests.
5.3

Word/Phrase
root
above ground
growthroot system
nitrogenunderground

Topic 307
DNA analysis
Word/Phrasebase sequencespecific
gene
dna
pcr
amplification

Results of an Application to Author Disambiguation

This subsection evaluates the applicability of the proposed†
Due to space limitations, only a set of the top three topic indices from each researcher is shown in this figure.

Prob.
0.04330.02720.02590.02340.02160.0177Prob.
0.0696
0.03180.02030.01530.01510.0130

Table 3 Details of the testing dataset used in an author disambiguation
experiment.
Number of papers
Number of researchers
Number of unique full names

6,632
345
166

method to the author disambiguation problem. For the experimental setup, we first extracted a set of researchers
who share the same names from the active researchers in
KAKEN. Then, we collected their publications from CiNii
Articles to construct a testing dataset for author disambiguation. The details of the testing dataset constructed are shownin Table 3. To conduct the experiment, we consider thepractical case, in which new papers are added to the target database day by day. The experiment procedure is asfollows: for a researcher name a, all papers of researchers
who share the name a, i.e., {d ∈ Ωr |r ∈ Ra }, were sorted
in the ascending order of the publication dates. Each researcher in Ra was assigned the first paper, and from this,the researchers topic vector was calculated. Then, taking
any other paper from the set, we selected the suitable author of the paper from Ra on the basis of the topic vectors
calculated by this time point. To calculate Eq. (2), we used

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Fig. 3 A Heat map of the topic representation of each researcher, in which only a set of top three
topic indexes from each researcher is shown due to the space limitation. Darker cells indicate a higher
assignment of the corresponding topic.

the cosine distance between two vectors. After author identification, the topic vector of the correct author was updated
according to Eqs. (3) and (4), and this process was iterated.
For performance comparison, we also applied the traditional vector space model (VSM) [27]. In VSM, each researcher is represented as the average vector of word vectors
from all publications. Specifically, the following three versions of VSM were applied:• VSM Stop. VSM Stop discards highly frequent words
and very rare words in the same way as in the proposed
method.
• VSM All. VSM All uses all words of papers without
removing stopwords.
• VSM TFIDF. VSM TFIDF uses all words of papers,
which are weighted with TF-IDF [28].
For reference, we compared the performance of our topicbased features with other metadata-based features, which
are presented in conventional studies [13], [29]. Specifically, we could investigate the eﬀectiveness of each of thefollowing fields of metadata only which is available in CiNii
Articles:• Aﬃliation. This feature is used in [13]. It is a tokenbased Jaccard similarity between the name of the aﬃliation written in a given paper and the name of a researcher’s latest aﬃliation.
• JCName. This feature is presented in [29]. It is atoken-based Jaccard similarity between the name of thejournal/conference that published a given paper and themost similar journal/conference name of past publications of a researcher.
• Title. This feature is presented in [29]. It is a tokenbased Jaccard similarity between the title of a given
paper and the most similar title of past publications ofTable 4 Mean accuracy of author disambiguation for the proposed
method and comparative methods.
Method
Aﬃliation
JCNameTitle
KeywordVSM Stop
VSM All
VSM TFIDF
Proposed method

Mean accuracy (standard deviation)
84.91% (10.92%)
91.99% (9.98%)
83.12% (11.95%)
59.58% (15.34%)
87.16% (13.16%)
89.27% (12.35%)
89.60% (12.35%)
92.60% (9.67%)

a researcher.
• Keywords. This feature is presented in [29]. It is also
a token-based Jaccard similarity between the keywords
of a given paper and the keywords from all publicationsof a researcher.
These features were used in the same experimental schemeas the VSM-based methods and the proposed method forfair comparison. Note that when each field of metadata wasempty or null for a given paper, we randomly selected its
author from the researchers, and averaged the accuracy of
five runs for the method.
To quantitatively measure the performance of eachmethod, we calculated the mean accuracy of author disambiguation over unique full names. The results are shownin Table 4, from which we find that the proposed method
achieved the best mean accuracy among all methods. This
result validates the eﬀectiveness of the textual features ofabstracts and its dimensionality reduction by LDA. We alsofind that JCName outperformed other metadata-based features, but its performance is not higher than that of our
method. This means that our topic representation cancharacterize each researcher’s domain better than the journal/conference names. The collaborative use of the pro-IEICE TRANS. INF. & SYST., VOL.E99–D, NO.4 APRIL 2016

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Fig. 4 A heat map of the topic representation of researcher pairs who share the same full name.
Darker cells indicate a higher assignment of the corresponding topic.

posed method and these metadata-based features will better identify the correct researchers, which should be investigated in future work.
For further examination, we randomly extract pairs of
researchers who share the same name and show their topic
vectors as a heat map in Fig. 4. Comparing Fig. 4 and Fig. 3(i.e., the heat map of researchers extracted from the same
research field), we cannot find many topic overlaps in eachpair of researchers having the same full names. This meansthat representing researchers’ interests in a topic space cancontribute to name disambiguation in a large-scale academic
database including all research fields.
In this experiment, we also found some diﬃcult casesthat cannot be distinguished by the proposed method. For
example, when two researchers who share the same fullname are experts in the almost same research theme, ourtopic representation cannot accurately disambiguate the author for a given paper. For example, distinguishing between
two researchers who specialize in agricultural research, cropscience, was diﬃcult for the proposed method. We observedthat in such a case, VSM TFIDF and VSM All worked
slightly better than the proposed method. From this observation, the distributions of stop words in a researcher’s
papers can be eﬀective to characterize his/her writing style.
Although our experiments removed stopwords that are generally irrelevant to the topics of papers, it could be useful tocombine topic information with distinctive stylistic features
for an author disambiguation problem. Thus, we will investigate the eﬀectiveness of the use of stopwords for further
improvement. Furthermore, to facilitate the management of
the academic database, we should develop not only author
identification among candidate researchers but also a framework that classifies whether the author is a new researcher
to register in the database. In our future work, we will introduce a novel scheme that classifies the authors of a given

paper into existing or new researchers to increase the usefulness of the proposed method.
6.

Conclusions and Future WorkIn this paper, we present a topic representation of researchers’ interests in a large-scale academic database. In
the proposed method, we first calculate topic distributions
over words by using LDA based on the training dataset.
Then, we convert all papers of a target researcher to topic
vectors by using LDA, and then calculate a centroid of the
topic vectors to characterize the researcher. The results ofexperiments conducted on the CiNii Articles show that the
topics extracted by our method actually span a diverse rangeof research fields. In the experiments, the applicability ofthe presented topic representation to author disambiguationis also demonstrated: our approach achieves a mean accuracy of 92.60%, which indicates that it outperforms comparative methods that directly use textual features without
dimensionality reduction or that are based on metadata.
Our work in this paper is the first step towards representing the researchers’ interests in a large-scale academic
database covering all research fields in Japan. Further room
for investigation exists. For example, although our quantitative evaluation showed the eﬀectiveness of simply averaging
researcher’s topic vectors, their weighted average or a more
complicated distribution such as Gaussian mixture models
might be suitable. How to summarize the researcher’s topic
vectors should be studied for further performance improvement.
In the experiments, we successfully extracted a set ofsensible topics from short documents like abstracts, as wellas the conventional methods on topic modeling [17], [24].
LDA has also been used to analyze other short documents,
e.g., IMDB movie plots or reviews [30], [31]. However,KATSURAI et al.: TOPIC REPRESENTATION OF RESEARCHERS’ INTERESTS IN A LARGE-SCALE ACADEMIC DATABASE

1017

some papers argued that extracting meaningful topics fromshort documents sometimes fails due to the lack of word cooccurrences [32]. To avoid the data sparsity problem caused
by very short abstracts, using external information resources
such as Wikipedia can reduce the sparsity [33]. In addition,
introducing additional variables such as journal/conference
names to topic modeling can also provide good features tocharacterize researchers. We will investigate the eﬀectiveness of additional features and information sources for topic
modeling in future work.
Adaptively combining the presented topic representation with bibliographic metadata will be eﬀective for improving the performance of author disambiguation by the
proposed method. Related to this, it will also be useful todevelop a novel topic model that can consider the personalities of the researcher’s writings. Furthermore, the availability of the presented topic representation is not limited
to author disambiguation. Other applications, including researcher network construction and collaborator recommendation, can be realized with the use of the researchers’ topic
vectors. In addition, visualizing the research theme transitions of a particular researcher or a research group can also
provide us new insights. Thus, our future work also includes
developing these applications to promote interdisciplinary
research and facilitate collaboration and research activities.
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Appendix
This appendix describes an overview of CiNii Articles, thesubject of this paper’s experiments. CiNii Articles is adatabase service maintained by the National Institute of Informatics that can search for information in academic articles published in academic society journals, university research bulletins or articles included in the National Diet
Library’s Japanese Periodicals Index Database [21]. As ofJune 23, 2015, it contains 19,042,439 articles, covering all
research fields including the humanities, law, economics,
pure sciences, engineering, agriculture, and medicine† . Duplicate information from diﬀerent databases can be treatedas a single article due to article identifiers.
In CiNii Articles, for each author name a, papers written by persons whose names are a are first clustered to discover groups of papers authored by identical researchers;
this is done in such a way that precision is kept very high.
Then, to accurately merge paper clusters for the same identical researcher, CiNii Articles uses a publication attachedto grant reports that have been submitted by researchers to
KAKEN. KAKEN is the awards database of the Grantsin-Aid for Scientific Research administered by the Ministry of Education, Culture, Sports, Science and Technology(MEXT) and the Japan Society for the Promotion of Science(JSPS). After each researcher who obtained a grant submits
a report with a publication list to KAKEN, the researcher islinked to clusters that include papers contained in the submitted publication list. Thus, the current system requires amanual procedure to associate researchers with papers. Notethat we can easily access the publication information andgrant information of a target researcher using Researcher
Name Resolver†† , which is a researcher identifier management system [15]. Figure A· 1 provides an overview of theresearcher identifiers in CiNii Articles, Researcher Name

Fig. A· 1 An overview of author identifiers in CiNii articles, researcher
name resolver, and KAKEN.
†††

http://ci.nii.ac.jp/cinii/servlet/DirTop?lang=en
http://rns.nii.ac.jp/

Resolver, and KAKEN. Because these databases cover all
research fields in Japan, there are many authors who sharethe same names. Thus, author disambiguation is a major issue that needs to be solved in order to provide information
about researcher activities. By developing our method (i.e.,
by representing researchers’ interests in a topic space), we
can facilitate the usability of searching the publications or
activities of a target researcher.

Marie Katsurai
received the B.S. degree
in Engineering, the M.S. degree and the Ph.D.
degree in Information Science and Technology
from Hokkaido University in Sapporo, Japan in
2010, 2012, and 2014, respectively. She wasa Research Fellow of the Japan Society for the
Promotion of Science from 2013 to 2015, andshe was with the National Institute of Informatics from 2014 to 2015. She is currently an Assistant Professor in the Department of Information
Systems Design, Doshisha University. Her research interests include multimedia information retrieval and data mining.
She is a member of the IEICE, IEEE, and ACM.

Ikki Ohmukai
received his Ph.D. degree
in informatics from the Graduate University for
Advanced Studies in 2005. He joined National
Institute of Informatics in 2005 and has been an
Associate Professor since 2009. His research interests are the semantic web and social media.
He is a member of IPSJ and JSAI.

Hideaki Takeda
is a professor at National Institute of Informatics (NII) Japan, anda professor at the Graduate University for Advanced Studies (Sokendai). He received B.Eng.,
M.Eng. and Dr.Eng. degrees in Precision Machinery Engineering from the University of Tokyo, Japan, in 1986, 1988 and 1991, respectively. He worked at the Norwegian Institute ofTechnology and the Nara Institute of Technology prior to joining the current institution. He
has been the Sumitomo Endowed Professor in
the University of Tokyo between 2005 and 2010. His research interests
include the semantic Web, knowledge sharing systems and design theory.