=Paper= {{Paper |id=Vol-3640/paper13 |storemode=property |title=Ten Years of Wikidata: A Bibliometric Study |pdfUrl=https://ceur-ws.org/Vol-3640/paper13.pdf |volume=Vol-3640 |authors=Houcemeddine Turki,Mohamed Ali Hadj Taieb,Mohamed Ben Aouicha,Lane Rasberry,Daniel Mietchen |dblpUrl=https://dblp.org/rec/conf/wikidata/TurkiTARM23a }} ==Ten Years of Wikidata: A Bibliometric Study== https://ceur-ws.org/Vol-3640/paper13.pdf

Ten years of Wikidata: A bibliometric study
Houcemeddine Turki1,∗ , Mohamed Ali Hadj Taieb1 , Mohamed Ben Aouicha1 ,
Lane Rasberry2 and Daniel Mietchen3,4
1
Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
2
School of Data Science, University of Virginia, Charlottesville, VA, United States of America
3
Ronin Institute for Independent Scholarship, Montclair, New Jersey, United States of America
4
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany

Abstract
In this research paper, we analyzed the scientific research dealing with Wikidata from its creation in
2012 until late 2022, as revealed by Scopus. We identified 945 relevant scholarly publications, mostly at
conferences. This landscape is characterized by small groups of experts and Wikidata contributors from
the Global North. The same applies to the funders of Wikidata research, which are mainly governmental
institutions from the Global North. Further networking and outreach should be done for better diversity
and inclusion inside the Wikidata research community. The analysis also finds an emphasis on research
around computer science perspectives on the development of Wikidata. Most outputs are mainly focused
on developing methods for the creation, enrichment, reuse, and evaluation of open knowledge graphs,
particularly Wikidata. However, there is also a significant but narrower interest in application-oriented
research about the use of Wikidata in digital humanities, biology, and healthcare.

Keywords
Wikidata, Open Knowledge Graphs, Bibliometrics, Wikimedia Research

1. Introduction
Wikidata’s influence in media and research ecosystems prompts questions and curiosity about
the nature and extent of its impact, reach, and user community. Many commentators have
presented their reactions to Wikipedia’s 2001 establishment and ongoing development [1]. Wiki-
data’s 2012 establishment included sharing website infrastructure and exchanging information
with Wikipedia [2, 3]. Soon after starting the flow of data between Wikipedia and Wikidata,
the next endeavor was connecting Wikidata with other structured data collections. The intent
was to promote knowledge transfer back and forth among Wikipedia, Wikidata, and the Linked
Open Data ecosystem, thus positioning the Wikimedia platform as a global hub for importing
and exporting general reference information [4, 5].

Wikidata’23: Wikidata Workshop at ISWC 2023
∗
Corresponding author.
Envelope-Open turkiabdelwaheb@hotmail.fr (H. Turki); mohamedali.hajtaieb@fss.usf.tn (M. A. Hadj Taieb);
mohamed.benaouicha@fss.usf.tn (M. Ben Aouicha); lr2ua@virginia.edu (L. Rasberry);
daniel.mietchen@ronininstitute.org (D. Mietchen)
Orcid 0000-0003-3492-2014 (H. Turki); 0000-0002-2786-8913 (M. A. Hadj Taieb); 0000-0002-2277-5814 (M. Ben Aouicha);
0000-0002-9485-6146 (L. Rasberry); 0000-0001-9488-1870 (D. Mietchen)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Proceedings
Commentaries review Wikidata from various perspectives, including general review of the
literature [6], topical coverage [7], quality of content [8], use in research libraries [9], and
use in digital humanities [10]. Although a 2014 paper took for granted that ”The relevance
of Wikidata for researchers in semantic technologies, linked open data, and Web science thus
hardly needs to be argued for” [4], these reports establish at a high level that researchers are
using Wikidata. However, understanding the extent and nature of researchers’ use of Wikidata
remains an open question. To address this gap, our study aims to explore the patterns and
trends in scientific research related to Wikidata for ten years (2012-2022) through an analysis
based on Scopus, one of the largest controlled bibliographic databases [11]. To achieve our
research objectives, we are focused on answering key questions. This includes analyzing usage
patterns to understand how frequently researchers integrate Wikidata into their work and
whether there is a noticeable upward trend over time. We are also investigating the primary
publication outlets for Wikidata-related research, identifying if specific academic journals or
conferences dominate in this regard. Furthermore, our research involves examining researcher
demographics, aiming to uncover who engages with Wikidata, including their nationalities and
affiliations. Lastly, we are exploring acknowledgments to determine the sponsors or funding
sources that researchers credit for supporting their Wikidata-related research endeavors.
Through addressing these questions, our study aims to offer a comprehensive overview of the
scholarly landscape surrounding Wikidata. We acknowledge the importance of understanding
Wikidata’s utilization and impact in research for assessing its role in advancing knowledge and
for identifying potential biases or areas for improvement in the Knowledge Graph. Our research
strives to provide valuable insights into how Wikidata is employed in academia, serving as a
foundation for further discussions and potential enhancements of this collaborative platform.

2. Method
We extract bibliographic metadata of research publications related to Wikidata between 2012
and 2022 as indexed by Scopus 1 . This is mainly done despite the better coverage of several other
bibliographic databases like OpenAlex, particularly because OpenAlex and other automatically
generated databases include several problems in author disambiguation and data formatting by
contrast to controlled bibliographic databases like Scopus that have consistent data modeling [12].
Also, controlled bibliographic databases verify that included scholarly venues meet research
integrity standards by contrast to automatically generated databases that include all kinds of
scholarly publications without proper validation [13]. It is true that controlled bibliographic
databases involve significant biases and can provide a distorted image of the scholarly production
on Wikidata [14]. However, this is the opportunity cost to pay for having a quality bibliometric
study with minor efforts and a limited allocated time.
The query extracts all the publications issued before 2023 that mention Wikidata in their
title, abstract, or keywords. These publications are verified by hand to eliminate irrelevant
papers. Extracted data involves author names, author and index keywords, source titles, years of
publication, affiliations, funding information, document types, conference names, and subject areas.
We use this information to generate statistical data about the characteristics of the considered
1
Query: (TITLE-ABS-KEY(Wikidata) AND PUBYEAR > 2011 AND PUBYEAR < 2023) .
Figure 1: Distribution of Wikidata-related research publications per year, according to Scopus.

publications using the Scopus user interface. Then, we generate the co-occurrence network of
the most common keywords and the co-authorship network of the most productive countries
in the dataset using VOSViewer, software for generating bibliographic networks [15]. We assign
weights to nodes based on their total link strength and we choose the colors of the nodes
based on their recency in the networks (so-called Overlay representation) where recency ranges
between -1 (oldest) and 1 (newest).

3. Results and Discussion
As of July 1, 2023, we identified 945 research publications related to Wikidata and indexed
by Scopus. These papers have been issued since the early days of Wikidata in 2012, and their
yearly productivity has linearly grown since 2014, reaching 188 publications in 2021 and 202
publications in 2022, as shown in Figure 1. This goes in line with the linear growth of the yearly
production of open knowledge graph research between 2013 and 2022 [16]. This growth is less
dynamic than that for knowledge graph research more generally, which is growing exponentially
every year [17]. More emphasis on open knowledge graph research should be done to achieve
yearly exponential growth, a characteristic of active and trendy topics in research [16].
When considering the types of Wikidata-related scholarly publications, we found that 78.1%
(738 out of 945) of them are proceedings papers, mostly showing original research and a limited
number of conference reviews, as shown in Figure 2. This finding seems to be applicable to
computer science research, in general, [18], particularly for knowledge graph-related topics
such as graph neural networks [19]. However, it does not seem to be valid for computer
science research in developing regions like Africa [20]. The dependency of the community
on conference papers rather than journal articles is motivated by the possibility of sharing
works under development and implementation during conferences, allowing the community
to coordinate their efforts regarding the ongoing development of Wikidata [18, 19]. That
being said, the Wikidata research community did not try shorter and easier types of journal
publications like letters to the editor that can stimulate discussion around the issues related to
the development of Wikidata as an open knowledge graph [21].
When seeing the target conferences for Wikidata-related research, we found that most of
Figure 2: Distribution of Wikidata-related research publications per document type.

Table 1
Top conferences publishing Wikidata-related scholarly research
Conference Abbreviation CORE Publications
International Semantic Web Conference ISWC A 68
International World Wide Web Confer- WWW A* 44
ence
Extended Semantic Web Conference ESWC A 42
Wikidata Workshop @ ISWC Wikidata N/A 41
Empirical Methods in Natural Language EMNLP A 22
Processing
Semantic Web Challenge on Tabular Data SemTab N/A 17
to Knowledge Graph Matching @ ISWC
International Symposium on Open Col- OpenSym C 17
laboration
Metadata and Semantics Research Con- MTSR N/A 14
ference
Language Resources and Evaluation Con- LREC C 14
ference
ACM International Conference on Infor- CIKM A 11
mation and Knowledge Management

them are specific ones dealing with semantic web and knowledge engineering as shown in
Table 1. According to the CORE 2021 Rankings, most of these research venues are classified
as top-tier ones, having a rating of A (e.g., ISWC and ESWC) or A* (e.g., WWW ) [22]. Despite
this, Wikidata researchers have also published significant contributions to CORE C conferences,
particularly OpenSym and LREC, as well as to several workshops at CORE A and A* conferences
(e.g., Wikidata and SemTab), where their topics of interest are closely related to the development
of the open knowledge graph. The ratings of these conferences have been the same as in
previous editions of CORE since 2013. These events serve as a forum for Wikidata researchers
to share and discuss their preliminary findings and source code [23].
As for the main journals that publish Wikidata-related research, we found that they are mainly
Table 2
Top journals publishing Wikimedia-related scholarly research
Journal Publications Publisher SJR
Semantic Web 22 IOS Press BV, Netherlands 0.828
Jlis.it 13 Università di Firenze, Italy 0.208
Database 7 Oxford University Press, 1.786
United Kingdom
Journal Of Web Semantics 7 Elsevier, Netherlands 0.955
Cataloging And Classification 4 Routledge, United States of 0.199
Quarterly America
IEEE Access 4 IEEE, United States of America 0.926
AIB Studi 3 Associazione Italiana Bib- 0.229
lioteche, Italy
Information Switzerland 3 MDPI, Switzerland 0.662
Information Systems 3 Elsevier Ltd., United Kingdom 0.976
International Journal Of Meta- 3 Inderscience Enterprises Ltd, 0.138
data Semantics And Ontologies United Kingdom
Journal Of Medical Internet Re- 3 JMIR Publications Inc., Canada 1.992
search
Komp’juternaja Lingvistika i In- 3 Komp’juternaja Lingvistika i In- 0.203
tellektual’nye Tehnologii tellektual’nye Tehnologii, Rus-
sian Federation
Nucleic Acids Research 3 Oxford University Press, 8.234
United Kingdom
Peerj Computer Science 3 PeerJ Inc., United States of 0.638
America

scholarly journals having a high citation impact (SJR > 0.8) [24] and dealing with semantic
technologies, database management, and information processing as shown in Table 2. However,
there are open-access mega-journals covering a wide range of research topics that also publish
Wikidata-related research like IEEE Access and PeerJ Computer Science. Furthermore, several
high-impact journals not related to computer science like Nucleic Acids Research can also be a
target for describing large-scale multidisciplinary applications of Wikidata. Moreover, scientists
also aim for national-level research journals that are closely related to database management
like Jlis.it, AIB Studi, and Komp’juternaja Lingvistika i Intellektual’nye Tehnologii to show their
preliminary results and to communicate the recent advances of Wikidata to local research
communities in their mother tongues [25]. This situation seems to be similar to the one of
research about knowledge graphs [17], specifically open knowledge graphs [16], except for the
use of nationwide specialized journals as targets for Wikidata-related research.
Most conferences and journals publishing Wikidata-related research are predominantly in
English, with 97.2% (919 out of 945) of Wikidata research publications being in English, reflecting
the prevailing language bias in scholarly research [26]. Open-access publications account for
only 32.3% (305 out of 945) of Wikidata-related scholarly research, primarily in the form of
green open access (235 publications) and a limited number of gold open-access papers (64
publications), possibly due to challenges related to open-access publication fees. Figure 3
Figure 3: Distribution of Wikidata-related research publications per funding sponsor.

illustrates limited support from the Wikimedia Foundation for open-access scholarly publishing
within its research community, with only 7 publications receiving such support. Major funders of
Wikidata-related research are government-led funding agencies located in developed countries,
including Germany (e.g., Deutsche Forschungsgemeinschaft and Bundesministerium für Bildung
und Forschung), the United States of America (e.g., National Science Foundation and Defense
Advanced Research Projects Agency), and countries with established traditions of semantic web
research like China (e.g., National Natural Science Foundation of China) and Chile (e.g., Fondo
Nacional de Desarrollo Científico y Tecnológico). Significant support also comes from the European
Commission and its Horizon Europe program (formerly Horizon 2020), a continent-level research
funding initiative. While these sponsors contribute significantly to Wikidata-related research,
their coverage is limited to specific countries. In areas beyond their reach, restrictions on
publishing updated research findings under permissive licenses often occur, especially when
research funding is scarce [27]. Funding agencies in developing regions tend to focus more
on capacity building and the latest computer science technology advancements rather than
large-scale knowledge engineering projects, particularly those related to open knowledge [28].
This bias in funding did not only affect open-access publishing but also research productivity
in underserved countries [29]. This is clearly revealed by the distribution of Wikidata-related
research publications per country as shown in Figure 4. Wikidata-related research is mainly
dominated by developed countries, mostly from Europe and North America, led by Germany
(239 publications) and the United States of America (175 publications). BRICS nations (i.e., Brazil,
Russian Federation, India, and China) are also among the most productive nations of Wikidata-
related scholarly research, ranked between 6th and 16th . A surprising fact is the standings
of Chile among the top countries in Wikidata-related research with 29 publications (Ranked
11th ). Despite its good standings in the Human Development Index2 , Chile is ranked 52nd in
computer science research productivity and 43rd in computer science citations [24]. Although
knowledge graph research is mainly dominated by developed countries, the standings of the
top countries in publishing knowledge graph research are quite different from the ones of the
same countries in publishing Wikidata-related scholarly research [17, 16]. China and the United
2
https://hdr.undp.org/data-center/country-insights#/ranks.
Figure 4: Distribution of Wikidata-related research publications per country.

States of America are the two main countries that dominate knowledge graph research [17],
particularly in the context of open knowledge graphs [16].
Germany’s prominence in Wikidata-related research is partly attributed to its close association
with Wikidata maintenance, overseen by Wikimedia Deutschland, the German chapter of the
Wikimedia Foundation [30]. The collaborative landscape of Wikidata research (Figure 5) reflects
its origins in partnerships between Germany and several European nations, including Italy,
Denmark, and Belgium, depicted in Purple. Non-European developed countries such as the
United States, Canada, and Australia entered the field later through extensive collaborations
with Germany and the United Kingdom, as indicated in Green. In recent years, BRICS countries
like China, Brazil, and India, Eastern European nations like Poland, East Asian countries like
South Korea, and developing nations such as Tunisia and Malaysia have also integrated into
Wikidata-related research, as shown in Yellow. This hierarchical collaboration network differs
from the general knowledge graph research landscape, where the collaboration between the
United States and China holds significant influence [17].
Germany’s dominant position is further evident in the list of top contributing institutions, with
seven of the 18 most productive institutions hailing from Germany (Figure 6). These institutions,
all universities and research institutes, showcase Germany’s robust presence in Wikidata-related
scholarly research. Other countries on the list typically have one representative institution,
except for France, having significant publications from Université de Lyon and the Centre
National de Recherche Scientifique, and the United States of America, featuring the University
of Southern California, its Information Sciences Institute, and two prominent AI corporations,
Google and IBM. The involvement of tech giants in Wikidata research may be attributed to
the United States’ orientation towards private sector-driven AI research [31]. The substantial
university involvement in European Wikidata-related research aligns with the research policies
of European countries, particularly Germany, which prioritize universities as key players in AI
research within the Triple Helix model framework [31].
When seeing the list of the main authors of Wikidata-related research, we found that most
of the universities that are among the most productive ones in Wikidata-related research are
included thanks to the efforts of individual scientists as shown in Table 3. Based on an examina-
Figure 5: Overlay representation of the co-authorship network for the top countries by number of
publications of Wikidata-related research. The oldest nodes are in purple. The newest ones are in yellow.

Figure 6: Distribution of Wikidata-related research publications per institution.

tion of Google Scholar profiles as of July 1, 2023, many of these scientists have a background in
knowledge engineering and an h-index > 30, as imported from Google Scholar profiles [32]. For
example, the status of Universidad de Chile and Universität Mannheim respectively as the first and
third most productive institutions mainly occurred thanks to the contributions of Aidan Hogan
and Heiko Paulheim. This information could be insightful for efforts to establish long-term
Wikidata research communities and traditions inside research institutions. Sometimes, younger
Table 3
Top authors publishing Wikidata-related scholarly research
Author and Institution Publications H-Index
Hogan, Aidan (Universidad de Chile, Chile) 26 40
Razniewski, Simon (Bosch Center for AI, Germany) 21 18
Simperl, Elena (King’s College London, United Kingdom) 20 42
Paulheim, Heiko (Universität Mannheim, Germany) 14 48
Szekely, Pedro (Information Science Institute, USC, United 14 40
States of America)
Darari, Fariz (Universitas Indonesia, Indonesia) 13 9
Diefenbach, Dennis (Université de Lyon, France) 13 15
Kaffee, Lucie-Aimée (University of Copenhagen, Denmark) 13 11
Waagmeester, Andra (Micelio, Belgium) 13 26
Ilievski, Filip (Information Science Institute, USC, United States 12 15
of America)
Schubotz, Moritz (University of Wuppertal, Germany) 12 21
Lehmann, Jens (Technische Universität Dresden, Germany) 11 63
Nutt, Werner (Free University of Bozen-Bolzano, Italy) 11 39
Gipp, Bela (University of Göttingen, Germany) 9 41
Mihindukulasooriya, Nandana (IBM Research, United States 9 17
of America)
Su, Andrew I. (Scripps Research Institute, United States of 9 67
America)

scientists having an h-index < 30 could be behind the establishment of research traditions
related to Wikidata development [32]. Successful examples are Simon Razniewski, Lucie-Aimée
Kaffee, Fariz Darari, and Dennis Diefenbach. This proves that Ph.D. or post-doc works can
catalyze the involvement of individuals and institutions in Wikidata-related research, and is
supporting evidence that early-career scientists can establish Wikidata research communities
and traditions in their scholarly institutions. Rarely, we can find several prolific authors of
Wikidata research that are also involved in the Wikimedia Community as active members of
the Wikimedia movement and as Wikidata, editors such as Andra Waagmeester and Andrew I.
Su. Such individuals are key for the development of research at the intersection of Wikidata
community priorities and semantic web challenges.
When dealing with the main scientific disciplines that shape Wikidata-related research, it is
clear that most of the publications deal with Wikidata research from the perspective of computer
science (812 publications), mathematics (229 publications), and engineering (54 publications),
as shown in Figure 7. This is a common characteristic of knowledge graph research [17]. The
development of generic algorithms for the enrichment and validation of knowledge graphs
is a priority to ensure the accuracy and consistency of such resources [17]. Beyond this, a
significant number of works have studied the practical applications of Wikidata in multiple
research areas. Most of these applications are related to Social Sciences (148 publications)
and Arts and Humanities (89 publications). Such works mainly aim for turning Wikidata into
a large-scale cultural heritage and archival database and using it to enhance free access to
Figure 7: Distribution of Wikidata-related research publications per subject area.

digital humanities data for research and development purposes [33, 34]. Considerable effort
has been provided to develop Wikidata applications for Decision Sciences (48 publications) and
Business, Management, and Accounting (23 publications). This is mostly linked to the study
and adjustment of user behavior and data governance when contributors are collaboratively
editing Wikidata [7]. Finally, there is a good amount of research work about using Wikidata
in Medicine (17 publications), Biochemistry, Genetics, and Molecular Biology (15 publications),
and Agricultural and Biological Sciences (10 publications). These works integrate biological
and medical knowledge into Wikidata and use them to develop systems to inform decisions in
medicine and biology such as dashboards and user interfaces [35].
The top featured keywords for the considered publications highlight the role of computer
science in Wikidata. Although the first four keywords represent a general terminology of the
topic as shown in Figure 8 (i.e., Knowledge Graphs, Wikidata, Semantic Web, and Knowledge
Graph), most of the top keywords show the main applications of Wikidata in computer science:
Knowledge-Based Systems (144 publications), Natural Language Processing Systems (123 publica-
tions), Knowledge Representation (81 publications), Computational Linguistics (71 publications),
Data Mining (69 publications), Question Answering (69 publications), Information Retrieval (47
publications), and Data Handling (46 publications). These applications are confirmed by previ-
ous studies on knowledge graph research [17], particularly the ones related to open knowledge
graphs [16]. Subsequently, the top keywords also reveal the main secondary resources that are
used to enrich and maintain Wikidata and comparatively evaluate its evolution: Wikipedia (129
publications), Linked Data (96 publications), Ontology (78 publications), DBpedia (70 publica-
tions), and Open Data (64 publications). Research trends on open knowledge graphs identify
Wikidata and DBpedia as the most studied open knowledge graphs that are comparatively eval-
uated [16]. The same trends reveal the development of methods to use Wikipedia, Ontologies,
and Linked Open Data for the construction of open knowledge graphs, particularly between
2013 and 2016 [16].
When examining the topics covered in Wikidata-related scholarly research through the
co-occurrence network of common keywords shown in Figure 9, we identified three stages in
the evolution of Wikidata research: The Creation stage (2012-2015, Purple) mainly focused on
demonstrating how Wikidata can serve as a collaborative knowledge graph to handle large-scale
Figure 8: Top keywords of the Wikidata-related scholarly research publications.

data. The Enrichment and Application stage (2016-2018, Blue-Green) aimed to enrich and assess
Wikidata using online sources like Linked Open Data, ontologies, and DBpedia. It also explored
applications of Wikidata in natural language processing, data classification, search engines,
knowledge management, and named entity recognition. Additionally, it analyzed the feasibility
of developing techniques for extracting semantic relations from text and integrating these
retrieved statements into Wikidata through entity linking. The Machine Learning and AI stage
(since 2019, Yellow) built upon previous research by focusing on the use of machine learning,
embeddings, and language models to enhance Wikidata. Researchers worked on advanced
applications of Wikidata, such as question answering, and aimed to predict missing statements
within Wikidata. These phases of Wikidata’s development coincided with similar stages in the
evolution of knowledge graph research over the past decade [17, 16]. Early knowledge graph
research primarily emphasized the development of semantic web standards and information
retrieval techniques, particularly in the context of applications in computational biology. Over
time, knowledge graph research expanded to support various disciplines, including natural
language processing and cultural heritage. This expansion also involved the adoption of query-
based methods like SPARQL and the incorporation of machine learning techniques, particularly
embeddings and graph learning [17, 16].

4. Conclusion
In this research paper, we identified 945 scholarly publications about Wikidata from 2012
to 2022, as indexed by Scopus. These works are mainly developed thanks to the personal
initiatives of several highly-cited scientists, Wikidata contributors, and early-career researchers
working in developed countries receiving funds from governmental institutions in Europe
and North America, rather than through the Wikimedia Foundation and other non-profit
Figure 9: Overlay representation of the co-occurrence network for the most common keywords of
Wikidata-related research. The oldest nodes are in purple. The newest ones are in yellow.

organizations. Many of these works emphasize the computer science perspective of the creation
and sustainability of Wikidata and to a lesser extent the application-oriented research about
the use of Wikidata in several fields like Molecular Biology, Digital Humanities, and Medicine.
The computer science-related works about Wikidata are keeping pace with the latest advances
in knowledge graph research (e.g., entity linking) and artificial intelligence (e.g., embeddings
and language models). In terms of limitations, this paper relied on a single database (Scopus) to
identify Wikidata-related research and did not explore other such databases, including Wikidata
itself. The paper did not analyze the dynamics behind research collaborations around Wikidata
either. As future directions of this research work, we intend to expand it by analyzing other
databases like Dimensions and by studying the correlation between the different characteristics
of the respective publications, including ones not studied in the present work, such as affiliations.

Acknowledgments
This research is funded by the Wikimedia Research Fund of the Wikimedia Foundation (San
Francisco, California, United States of America) through the Adapting Wikidata to support
clinical practice using Data Science, Semantic Web and Machine Learning Project. Source data is
made available upon request.
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