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PAPER

Special Section on Data Engineering and Information Management

Interdisciplinary Collaborator Recommendation Based on
Research Content Similarity
Masataka ARAKI†a) , Nonmember, Marie KATSURAI†b) , Member, Ikki OHMUKAI††c) , Nonmember,and Hideaki TAKEDA††d) , Member

SUMMARY
Most existing methods on research collaborator recommendation focus on promoting collaboration within a specific disciplineand exploit a network structure derived from co-authorship or co-citation
information. To find collaboration opportunities outside researchers’ own
fields of expertise and beyond their social network, we present an interdisciplinary collaborator recommendation method based on research content
similarity. In the proposed method, we calculate textual features that reflect a researcher’s interests using a research grant database. To find themost relevant researchers who work in other fields, we compare constructing a pairwise similarity matrix in a feature space and exploiting existingsocial networks with content-based similarity. We present a case study
at the Graduate University for Advanced Studies in Japan in which actual collaborations across departments are used as ground truth. The results indicate that our content-based approach can accurately predict interdisciplinary collaboration compared with the conventional collaboration
network-based approaches.
key words: interdisciplinary research, collaborator recommendation, academic database analysis

1.

Introduction

Research collaboration can have a positive impact on scientific productivity [1], [2]. Interdisciplinary collaboration
among researchers with diﬀerent areas of expertise can potentially produce innovative ideas, solutions, and technologies beyond existing frameworks [3], [4]. However, due
to a lack of understanding of other fields and communication opportunities across fields, it is generally diﬃcult for
researchers to find new collaborators outside their own areas of expertise. To encourage collaboration, an automatic
method for recommending relevant researchers is necessary.
Most state-of-the-art methods for collaborator recommendation use the bibliographic data of papers for finding relevant researchers [5]–[7]. For example, co-authorshipnetworks are derived from author lists of papers to which
network analysis methods are applied to find prospective
collaborators for a target researcher [6], [7]. Such an existing network-based approach is called a friend on friends
approach, which is an application of link prediction in social
Manuscript received June 29, 2016.
Manuscript publicized October 13, 2016.
†
The authors are with Doshisha University, Kyotanabe-shi,
610–0394 Japan.
††
The authors are with the National Institute of Infomatics,
Tokyo, 101–8430 Japan.
a) E-mail: araki@mm.doshisha.ac.jpb) E-mail: katsurai@mm.doshisha.ac.jpc) E-mail: i2k@nii.ac.jpd) E-mail: takeda@nii.ac.jp
DOI: 10.1587/transinf.2016DAP0030

networks [8]. Although conventional methods have been
evaluated using sub-domains within a specific discipline,
their eﬀectiveness for recommending collaborators acrossdiﬀerent fields has not been adequately investigated. In
addition, depending on existing collaboration relationships
might result in losing a new collaboration opportunity. To
bridge the gap between diﬀerent fields beyond the social networks, we need identify potential collaborators who work in
diﬀerent fields but have similar research interests.
This paper presents an interdisciplinary collaborator
recommendation method based on research content similarity. Our method consists of two steps: calculating researchers’ feature vectors and computing the relevance between researchers. In the first step, we characterize the interests of each researcher using textual documents in a research database. To cover all research fields, we use research reports in the Database of Grants-in-Aid for Scientific Research, named KAKEN∗ , which includes information about research grants provided from the Japan Society
for the Promotion of Science (JSPS). From each report, atitle, abstract, and a set of research keywords are used toform our method’s textual features. Textual features of allreports associated with a researcher are then summarized toa feature vector of that researcher. In the second step, givena target researcher, we find the most relevant researchers
from other fields. However, it is challenging to identify aresearcher’s field without self-assessment; thus, we assume
that the department to which each researcher belongs represents his/her research field and consider cross-department
recommendation as an alternative. To recommend relevant
researchers who work in other departments, we provide two
types of strategies: pairwise similarity calculation in a feature space and similarity propagation in an existing collaboration network.
To evaluate the performance of interdisciplinary collaborator recommendation, we focus on collaboration examples across diﬀerent departments in the Graduate University
for Advanced Studies (SOKENDAI) in Japan. SOKENDAI
has various departments corresponding to research institutes
with active researchers who have obtained funding, and thus
it is suitable as a case study. Results of the experiments showthat our content-based approach achieved better recommendation results than the conventional methods.
The main contributions of this study are twofold. First,∗

https://kaken.nii.ac.jp/

c 2017 The Institute of Electronics, Information and Communication Engineers
Copyright IEICE TRANS. INF. & SYST., VOL.E100–D, NO.4 APRIL 2017786

we present a new attempt to analyze interdisciplinary collaboration at a university and a computational approach topromote it using a research grant database. Second, in
the interdisciplinary collaborator recommendation task for
SOKENDAI, we present a comprehensive comparison between the content-based approach and the conventional collaboration network-based approach with available evaluation metrics. Results of the experiments provide us new insights about the advantages of the content-based approachfor facilitating interdisciplinary research. The remainder ofthis paper is organized as follows: Section 2 provides abrief review of related studies. Section 3 describes an interdisciplinary collaborator recommendation method using
research content similarity. Section 4 presents the analysisof the current collaboration in SOKENDAI and the experiments to verify the eﬀectiveness of the proposed method.
Finally, Sect. 5 provides conclusions and suggests possible
directions for future work.
2.

Related WorkTo facilitate research activities, automatic collaborator recommendation methods have been presented [5]–[7], [9],[10]. Gollapallis et al. [9] presented a similar researcher
recommendation method based on researcher profiles calculated from academic homepages. Tang et al. [10] constructed a researcher network using a topic model thatlearns a set of topics from collaboration examples and evaluated its performance within five sub-domains related tocomputer science. Since the computational cost of themethod [10] depends on the number of combinations of research fields, it is diﬃcult to perform when multiple disciplines are targeted. Collaborator recommendation methods
have also been developed as an application of social network analysis techniques [5]–[7]. This approach first derives a researcher network from the bibliographic data ofpapers and then calculates relevant scores of researchers
against a target researcher. To calculate the relevance
scores of researchers in the network, Random Walk with
Restart (RWR) [11] is often exploited. Specifically, Huynh
et al. [5] used several features, including a citation networkbased rating of researcher importance. Li et al. [6] constructed a co-authorship network and weighted an edge between two researchers with their co-authorship frequency.
Guo et al. [7] combined a co-citation network with a coauthorship network to recommend collaborators. These
conventional methods have been evaluated within specificfields, such as computer science-related sub-domains whose
papers are included in DBLP [12].
The importance of interdisciplinary collaboration has
been emphasized by many institutions. Because universities
and research institutes have been divided into departments
according to research content, lowering barriers to crossboundaries among the departments is necessary to facilitate
interdisciplinary research [13], [14]. Thus, it is reasonable
that our work considers relevant researchers who belong toother departments to establish interdisciplinary collabora-

tion. To the best of our knowledge, this paper is the first to
focus on collaboration across departments of a university inJapan. To analyze interdisciplinary collaboration patterns, alarge-scale research database that covers researchers in all
fields is required. Nichols [15] used grant proposals andawards in the National Science Foundation (NSF) database†to quantify the interdisciplinarity. NSF is the only federal
funding agency that supports all of the basic sciences, andexamining it allows for an assessment across all disciplines.
In Japan, KAKEN plays a similar role, which includes research reports of projects awarded by MEXT/JSPS†† . Thus,
our study uses KAKEN as an information source to model
the researchers in several departments of SOKENDAI andanalyze their collaborations.
3.

Interdisciplinary Collaborator Recommendation
Based on Research Content Similarity

This section presents an interdisciplinary collaborator recommendation method based on research content similarity.
Figure 1 presents an overview of the proposed method. As
shown, we first calculate a feature vector that reflects the interest of each researcher using textual documents in the research grant database (see Sect. 3.1). Then, we calculate thesimilarity between researchers using the features and recommend interdisciplinary collaborators (see Sect. 3.2).
3.1 Researcher Feature Vectors and Similarity Measures
To characterize a researcher’s interests, a set of textual documents authored by the researcher should be obtained. It is
usually diﬃcult to prepare a common information source formodeling researchers because research cultures, such as citations and publications are diﬀerent across disciplines [16].
To solve this problem, we propose to use a research grant
database that oﬀers research reports in a common format
for all research fields. In KAKEN, each researcher is assigned a unique researcher number and linked to research
projects [17]. Each project is also assigned a unique project
number and associated with all project members (i.e., researchers), title, research period, research field, and keywords. A principal researcher of each project normally submits an annual research report, and only the latest reportbecomes available as an abstract of the project. The research keywords are manually identified by the principal researcher, which gives a short description of the project. Notethat our method can be applied to an arbitrary database that(i) covers all research fields and (ii) is equipped with author
disambiguation.
In our framework, features of researcher expertise canbe modeled using various representations, e.g., a word frequency vector and its low-dimensional vector. Below, we
present these features and their similarity measures.
Bag-of-Words (BoW) representation. BoW extracts†††

http://www.nsf.gov/
https://www.jsps.go.jp/english/e-grants/

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Fig. 1

An overview of interdisciplinary collaborator recommendation using a research database.

a set of words from all research projects associated with atarget researcher. There are various measures to computethe similarity between two sets of words. First, we use a
Jaccard similarity to compute the word set-based similarity
between two researchers i and j as follows:
sim jac (i, j) =|Wi ∩ W j |,|Wi ∪ W j |(1)where Wi represents a set of unique words for researcher iand |W| is the cardinality of W.
It is well known that applying term frequency-inverse
document frequency (TF-IDF) [18] to a set of words is eﬀective for considering the importance of each word in a target
corpus. Let V represent a vocabulary of unique words in adataset, and represent the TF-IDF vector for researcher i by
xi = [xi1 , xi2 , · · · , xi|V| ]. Then, the weighted Jaccard similarity [19] between two vectors xi and x j can be computed asfollows:
|V|k k
k=1 max(xi , x j )simw jac (i, j) = |V|
.
(2)
k k
k=1 min(xi , x j )We compare the above measures with the cosine similarity
between two TF-IDF vectors
xi · x j.
(3)
simcos (i, j) =
xi x j Topic representation. Recent studies exploit probabilistic topic models, such as Latent Dirichlet Allocation(LDA) to represent the researchers’ interests [15], [20]–[22].
The calculation of topic distributions corresponds to reducing the dimensionality of the textual feature space andfinding semantic word clusters. Topic representation has
been eﬀectively used for collaboration analysis [20], research community detection [21], and author disambiguation [22]. We investigate its eﬀectiveness for the interdisciplinary collaborator recommendation task, in which thesame feature extraction approach in [22] is exploited. We
apply LDA to a training dataset to produce word distributions for each topic. Using the learned LDA, we represent projects of each researcher by topic distributions andthen average them to construct a feature vector of that researcher. The similarity between topic representations ofresearchers is also computed using cosine similarity as defined in Eq. (3).
In our case study, we investigated the performance ofFig. 2 Two strategies of interdisciplinary collaborator recommendation
using a collaboration network.

each combination of feature vectors and similarity measures.
3.2 Relevant Researcher Recommendation for Interdisciplinary Research
Recent methods exploit social networks around researchers
to find future collaborators without expertise modeling [6].
In contrast, our assumption is that for promoting interdisciplinary research, diﬀerent fields should be bridged using
content-based potential relevance. In our method, as shownin Fig. 2, we compare the following two recommendation
strategies that exploit content-based similarity between researchers.
Strategy I: Calculating pairwise similarities in afeature space. This strategy recommends interdisciplinary
collaborators based on research content only. Pairwise similarities between researchers are computed using one of themeasures described in Sect. 3.1. For a target researcher, top
K researchers who have high similarities and work in otherdepartments are recommended.
Strategy II: Weighting existing collaboration network with research content similarity. This strategy recommends interdisciplinary collaborators based on both social relationships and research content similarities. Similar
to the conventional method [6], a collaboration network is
constructed in which each node corresponds to a researcher,and an edge is depicted if two researchers share at least one
project. Then, each edge weight is computed as a similar-

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ity between the corresponding researchers using one of themeasures described in Sect. 3.1. Given a target researcher,relevant researchers are determined using RWR in the constructed network. Let S be a row-normalized state transition matrix whose (i, j)-th entry denotes the normalized edge
weight between researchers i and j. In RWR, the relevance
scores of the researchers are updated at the t-th iteration using the following equation:r(t+1) = αSr(t) + (1 − α)q,(4)

where r(t) is a vector whose i-th entry denotes the relevance
score of researcher i; q is the initial vector, which sets ascore of the target researcher to one and scores of other researchers to zero; α ∈ [0, 1] is a parameter that balances thecontribution of the initial vector and the researcher relationships. By sorting the scores, the top K researchers belonging
to other departments are presented to the target researcher asinterdisciplinary collaborators.
Comparing Strategies I and II, we can investigate theinfluence of the existing collaboration network on finding
opportunities for interdisciplinary research.
4.

in KAKEN. As of April 2015, 1,227 faculty members belonged to SOKENDAI, among which 1,081 researchers (17of these belong to more than one department) had unique researhcer numbers in KAKEN. Using each researcher number as a query, we extracted 7,698 research projects fromKAKEN. If two researchers share at least one project, thenwe regarded them as having a collaborative relationship.
Consequently, 1,331 projects were collaboration projects by
SOKENDAI researchers. The “interdisciplinary collaboration relationship” was established as ground truth only when
the two researchers belonged to diﬀerent departments. The
constructed dataset associated with project titles, members,
research keywords, and abstracts is denoted as the KAKEN
dataset.
In Sect. 4.1, we first analyze the current situation ofcollaboration across departments at SOKENDAI by visualizing the collaboration relationships. In Sect. 4.2, we investigate the eﬀectiveness of each feature extraction method in
the proposed method. In Sect. 4.3 we compare performance
between the content-based approach and the collaboration
network-based approach.
4.1 Current Situation of Interdisciplinary Collaboration at
SOKENDAI

Evaluation and Analysis

In this section, we present a case study at SOKENDAI toverify the eﬀectiveness of our content-based approach forinterdisciplinary collaborator recommendation. Each faculty member of SOKENDAI belongs to at least one department, and a department usually corresponds to a specificsubject or discipline. Table 1 shows a list of all departments and their numbers of researchers that are registeredFirst, we analyzed a current situation of collaboration across
SOKENDAI departments using the KAKEN dataset. Figure 3 shows a collaboration network depicted using theforce-directed graph-drawing algorithm in which a nodecorresponds to a researcher and an edge represents a col-Table 1 List of departments in SOKENDAI and their numbers of researchers found in KAKEN (numbers of researchers refer to those who
also belong to other departments).
Department
National Museum of Ethnology (Minpaku)
International Research Center for Japanese Studies(Nichibunken)
National Museum of Japanese History (Rekihaku)
The Open University of Japan Center for Open Distance Education (OUJC)National Institute of Japanese Literature (NJIL)
Institute for Molecular Science (IMS)
National Astronomical Observatory (NAOJ)
National Institute for Fusion Science (NIFS)
Institute of Space and Astronautical Science (ISAS)
Accelerator Laboratory (KEK)
Institute of Materials Structure Science (IMSS)
Institute of Particle and Nuclear Studies (IPNS)
Institute of Statistical Mathematics (ISM)
National Institute of Polar Research (NIPR)
National Institute of Informatics (NII)
National Institute of Genetics (NIG)
National Institute for Basic Biology (NIBB)
National Institute for Physiological (NIPS)
School of Advanced Sciences (SAC)The Center for the Promotion of Integrated Sciences(CPIS)
Total# of researchers
45
20
32
13
25
60
96(1)
59
74
134(1)
55
101
45(1)
50
65
60
57(1)
62(1)
16(6)
4(6)
1,073(17)Fig. 3 Visualization of a collaboration network constructed from the
KAKEN dataset. The node size is proportional to the number of projects
associated with the researcher, and the node color corresponds to the researcher’s department. The number of nodes is 1,081, and the number ofedges is 2,107.

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laboration relationship. The node size is proportional to thenumber of projects associated with the researcher, and thenode color corresponds to the researcher’s department. If
a researcher belongs to more than one department, then the
researcher was associated with the department with fewer
members. In this network, 577 nodes out of 1,081 nodes belong to the largest graph, while there are 288 isolated nodes.
For easier viewing, we manually drew a circle with an abbreviated department name to indicate a cluster of the department members. As shown, most collaborations were
within department. There were only 222 interdisciplinary
collaboration projects; this corresponds to about 2.9% of thetotal number of projects in the KAKEN dataset. This visualization indicates that the connection between the departments is not dense. Link prediction based on the existing
collaboration network might suﬀer from the small numberof positive examples.
4.2

Evaluation of Feature Vectors

This subsection evaluates the performance of each typeof researcher feature vector and similarity measure. In
this experiment, we used 222 interdisciplinary collaboration
projects as a testing dataset and the remaining 7,476 projects
as a training dataset. Before feature extraction, because texts
in KAKEN have been registered using OCR from prints
of reports, we corrected any OCR-based minor errors using KAKEN-specific normalization† . In addition, we usedpreprocessing techniques, such as NFKC normalization forJapanese UTF-8 texts, converting all uppercase characters
to lowercase characters and removing URL strings. Then
we applied morphological analysis to the project titles andabstracts, using MeCab†† ; in this analyzer, we introduced alist of all research keywords in the dataset and a list of page
names in the Japanese Wikipedia††† to consider named entities. Finally, we used only nouns (including named entities)
from the title and abstract of each project and its research
keywords as word features of the project. For topic representation (i.e., LDA-based feature vectors), we followed thesame setting in [22]; the number of topics was empirically
set to 500, and LDA hyperparameters α and β were set to
0.1 and 0.01, respectively. The words that appeared lessthan three times in all texts or appeared in more than 50%of the documents were removed in LDA training.
Each type of researcher feature vector was calculatedusing the training dataset, and relevant researchers were recommended to each researcher in the testing dataset using
Strategy I that exploits pairwise similarities. If the recommended researcher was an actual collaborator, then we regarded it as a true positive. As evaluation metrics, we used
an averaged precision of the top K recommended results foreach researcher (denoted as Prec@K) and an averaged recall†
For example, some long vowel symbols were registered as hyphens and only whitespaces were sometimes included as research
keywords. These errors were fixed before feature extraction.
††
http://taku910.github.io/mecab/†††
https://ja.wikipedia.org/

Fig. 4
Averaged precision and recall of the top K recommendations foreach researcher using each type of researcher feature vector: (a) Prec@K
and (b) Recall@K.

of the top K recommended researchers for each researcher(denoted as Recall@K).
Figure 4 shows the recommendation results of eachcombination of researcher feature vector and similarity measure. As shown, the weighted Jaccard similarity gave better results than the Jaccard similarity, which indicates theeﬀectiveness of TF-IDF weighting. TF-IDF vectors withcosine similarity achieved the best Prec@K and Recall@K
for every K. Although LDA-based representation has been
useful for several applications, such as author disambiguation [22], it could not outperform the TF-IDF features in thissetting. We suggest this is because a slight overlap between
researchers’ interests was missed due to the dimension reduction of word features to topics. Thus, we should be careful about using dimension reduction methods for finding thepotential relevance of diﬀerent disciplines. This observation
will be useful for developing a sophisticated recommendation model for interdisciplinary collaboration.

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4.3

Performance Comparison among Content-Based andCollaboration Network-Based Approaches

Next, we present a performance comparison to investigate
whether the existing collaboration network is beneficial forinterdisciplinary collaborator recommendation. Similar toconventional studies [6], [10], this experiment was designed
to predict future collaborations from historic collaboration
examples. We divided the KAKEN dataset into two parts:
4,103 projects before 2004 as a training dataset and 3,595
projects since 2005 as a testing dataset. The numbers of collaboration examples in the training and testing datasets were
119 and 103, respectively. If an interdisciplinary collaborator was accurately recommended to a testing researcher
using the training dataset, then we regarded the recommendation as a true positive. Note that the evaluation was performed for only 73 researchers who had at least one interdisciplinary collaborator in the testing dataset and also had
a path to the collaborator in the training dataset. We used
Prec@K and Recall@K as evaluation metrics and compared
the following listed methods.
Adamic/Adar [23]. This method has been utilized forlink prediction in co-authorship networks [8]. It computes
a similarity between two researchers i and j based on theircommon collaborators as follows:

1
,
(5)
Adamic/Adar(i, j) =
log |Γ(z)|z∈Γ(i) Γ( j)where Γ(i) is a set of researchers adjacent to the researcher
i in the collaboration network. This method corresponds
to exploiting the local structure of an original collaboration
network without edge weights.
ACRec mod.
The collaborator recommendation
method presented in [6], called ACRec, weights an edge between two researchers using the bibliographic data of theirco-authored publications, such as the co-author order, latest collaboration time point, and the total times of collaboration. Because KAKEN does not include the data of coauthor order for a project, we omitted the co-author orderfrom the original ACRec and denote it by ACRec mod. The
relevant scores of researchers are calculated using RWR in
the weighted network.
Content. This method uses Strategy I, which recommends interdisciplinary collaborators using pairwise similarities among researchers in a feature space.
Network+Content. This method uses Strategy II,
which corresponds to weighting the existing collaboration network with research content similarity. The relevant scores of researchers are calculated using RWR in theweighted network.
Note that in Content and Network+Content, we used
TF-IDF vectors and cosine similarity, which achieved thebest performance in the previous experiment. Prior to comparison, we had to investigate the influence of parameter α
for RWR in ACRec mod and Network+Content. Figure 5

Fig. 5
Averaged precision and recall of the top K (K = 5, 10) recommendations for Network+Content and ACRec mod when changing α inthe RWR: (a) Prec@K and (b) Recall@K.

shows Prec@K and Recall@K (K = 5, 10) for these two
methods when α changes in the RWR. Although the valueof α is generally set to 0.2 in social network analysis, a larger
α tends to produce slightly better results, which implies the
speciality of this collaboration network. We chose α = 0.9
for performance comparison.
Figure 6 shows Prec@K and Recall@K for all methods. Note that because the ground truth was constructedusing the actual collaborations, there is a possibility thatthe recommended collaboration is fundamentally correct
and Prec@K should be higher in such a case. Thus,
Recall@K can be more reliable in our experiments. Specifically, Content often produced better Recall@K than Network+Content, which means Strategy I can find relevant
researchers beyond the existing network. As shown, ourcontent-based methods, i.e., Content and Network+Content,
often achieved better results than collaboration networkbased methods, which highlights the importance of researcher expertise modeling. From the results, we consider

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Fig. 6
Averaged precision and recall of the top K recommendations foreach researcher using each method: (a) Prec@K and (b) Recall@K.

that focusing on research content similarity is more beneficial for the interdisciplinary collaborator recommendation
than depending on the existing collaboration network. This
matches well with the assumption that interdisciplinary collaborators cannot always be found in historical social networks. These findings from our case study will help develop
computational approaches to promote interdisciplinary research.
5.

Conclusions and Future WorkIn this paper, we present an interdisciplinary collaborator
recommendation method based on research content similarity. In the proposed method, we first calculated researcher
feature vectors using research reports in KAKEN. Then, tofind relevant researchers who work in other departments,
we presented two types of strategies that exploit contentbased similarity: Strategy I based on pairwise similarities
and Strategy II based on the collaboration network with research content similarities. A case study at SOKENDAI re-vealed fewer collaborations across departments compared tocollaborations within departments.
In our content-based approach, TF-IDF vectors produced better results than LDA-based topic representation
for interdisciplinary collaborator recommendation. Thissuggests a diﬃculty in dimension reduction for findinga slight overlap between diﬀerent disciplines, which isuseful knowledge for developing a sophisticated recommendation model in future work. Comparing Strategy II
to ACRec mod (a modified version of the conventional
method [6]), we observed that research content similarities
are more eﬀective as edge weights in the collaboration network than the collaboration frequency. Strategy I based on
pairwise similarities achieved better recommendation performance than the collaboration network-based approaches.
From the results, we conclude that the research content similarity has a significant influence on interdisciplinary collaborator recommendation beyond the existing social relationship.
Our work in this paper is the first to focus on how
to bridge individual departments for promoting interdisciplinary research. Further room for investigation and improvement exists in our study; for example, how to characterize the researchers’ interests should be studied for better recommendations. Based on the results obtained, we
will investigate additional information sources and develop
a sophisticated model for eﬀective recommendation. Due tothe small number of positive examples, failure recommendations by our method might be collaborations that should
actually be established but have not been performed yet. A
user study with the cooperation of faculty members of universities is eﬀective for providing the ground truth. According to [24], the interdiscipline of a research project can beevaluated by three metrics, i.e., variety, balance, and disparity. Such an analysis for each project can contributeto characterize a researcher’s interdiscipline using historical projects. Our future work also includes recommending
interdisciplinary research groups and research themes.
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Masataka Araki
received the B.S. degree inEngineering from Doshisha University in 2016.
He is currently a student in the Graduate Schoolof Science and Engineering, Doshisha University. His research interests include academic
database analysis.

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, and a 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 of Technology 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.