=Paper=
{{Paper
|id=Vol-1878/article-03
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1878/article-03.pdf
|volume=Vol-1878
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==None==
https://ceur-ws.org/Vol-1878/article-03.pdf
Scholia and Scientometrics with Wikidata
Finn Årup Nielsen1 , Daniel Mietchen2 , and Egon Willighagen3
1
Cognitive Systems, DTU Compute, Technical University of Denmark, Denmark
2
EvoMRI Communications, Jena, Germany
3
Dept of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, The
Netherlands
Abstract. Scholia is a tool to handle scientific bibliographic information
through Wikidata. The Scholia Web service creates on-the-fly scholarly
profiles for researchers, organizations, journals, publishers, individual
scholarly works, and for research topics. To collect the data, it queries the
SPARQL-based Wikidata Query Service. Among several display formats
available in Scholia are lists of publications for individual researchers and
organizations, publications per year, employment timelines, as well as co-
author and topic networks and citation graphs. The Python package im-
plementing the Web service is also able to format Wikidata bibliographic
entries for use in LaTeX/BIBTeX.
1 Introduction
Wikipedia contains significant amounts of data relevant for scientometrics, and
it has formed the basis for several scientometric studies [5,12,15,16,21,24,27].
Such studies can use the structured references found in Wikipedia articles or use
the intrawiki hyperlinks, e.g., to compare citations from Wikipedia to scholarly
journals with Thomson Reuters journal citation statistics as in [15] or to rank
universities as in [27].
While many Wikipedia pages have numerous references to scientific articles,
the current Wikipedias have very few entries about specific scientific articles. This
is most evident when browsing the Academic journal articles category on the
English Wikipedia.4 Among the few items in that category are famed papers such
as the 1948 physics paper The Origin of Chemical Elements [2] – described in
the English Wikipedia article Alpher–Bethe–Gamow paper 5 – as well as the 1953
article Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic
Acid [4] on eight Wikipedias. Another scientific article is Hillary Putnam’s Is
Semantics Possible? [22]6 from 1970 on the Estonian Wikipedia.
This article is available under the terms of the Creative Commons Attribution 4.0
License.
4
https://en.wikipedia.org/wiki/Category:Academic_journal_articles
5
https://en.wikipedia.org/wiki/Alpher%E2%80%93Bethe%E2%80%93Gamow_paper
6
https://et.wikipedia.org/wiki/Is_Semantics_Possible%3F
References in Wikipedia are often formatted in templates, and it takes some
effort to extract and match information in these fields. For instance, in a study
of journals cited on Wikipedia, a database was built containing journal name
variations to match the many different variations that Wikipedia editors used
when citing scientific articles [15]. The use of standard identifiers – such as the
Digital Object Identifier (DOI) – in citations on Wikipedia can help to some
extent to uniquely identify works and journals.
Several other wikis have been set up to describe scientific articles, such as
WikiPapers, AcaWiki, Wikilit [19] and Brede Wiki [17].7 They are all examples of
MediaWiki-based wikis that primarily describe scientific articles. Three of them
use the Semantic MediaWiki extension [13], while the fourth uses MediaWiki’s
template functionality8 to structure bibliographic information.
Since the launch of Wikidata9 [25], the Wikimedia family includes a platform
to better handle structured data such as bibliographic data and to enforce in-
put validation to a greater degree than Wikipedia. Wikidata data can be reified
to triples [8], and graph-oriented databases, including SPARQL databases, can
represent Wikidata data [9]. The Wikidata Query Service (WDQS)10 is an ex-
tended SPARQL endpoint that exposes the Wikidata data. Apart from offering
a SPARQL endpoint, it also features an editor and a variety of frontend result
display options. It may render the SPARQL query result as, e.g., bubble charts,
line charts, graphs, timelines, list of images, points on a geographical map, or
just provide the result as a table. These results can also be embedded on other
Web pages via an HTML iframe element. We note that Wikidata is open data
published under CC0, and it is available not only through the SPARQL end-
point, but also—like any other project of the Wikimedia family—through an
API and dump files.11
In the following sections, we describe how Wikidata has been used for bib-
liographic information and present Scholia, our website built to expose such
information. We furthermore show how Scholia can be used for bibliography
generation and discuss limitations and advantages with Wikidata and Scholia.
2 Bibliographic information on Wikidata
Wikidata editors have begun to systematically add scientific bibliographic data
to Wikidata across a broad range of scientific domains – see Table 1 for a sum-
mary of Wikidata as a digital library. Individual researchers and scientific arti-
cles not described by their own Wikipedia article in any language are routinely
7
http://wikipapers.referata.com/, https://acawiki.org/, http://wikilit.
referata.com/ and http://neuro.compute.dtu.dk/wiki/
8
https://www.mediawiki.org/wiki/Help:Templates
9
https://www.wikidata.org
10
https://query.wikidata.org
11
The API is at https://www.wikidata.org/w/api.php, and the dump files are avail-
able at https://www.wikidata.org/w/api.php.
12
https://github.com/larsgw/citation.js
Dimension Description
Domain Broad coverage
Size > 600, 000 scientific articles
Style of Metadata Export via, e.g., Lars Willighagen’s citation.js12
Persistent Inbound Links? Yes, with the Q identifiers
Persistent Outbound Links Yes, with identifiers like DOI, PMID, PMCID, arXiv
Full Text? Via identifiers like DOI or PMCID; dedicated property
for ‘full text URL’
Access Free access
Table 1. Summary of Wikidata as a digital library. This table is directly inspired by
[10, Table 1].
added to Wikidata, and we have so far experienced very few deletions of such
data in reference to a notability criterion. The current interest in expanding bib-
liographic information on Wikidata has been boosted by the WikiCite project,
which aims at collecting bibliographic information in Wikidata and held its first
workshop in 2016 [23].
The bibliographic information collected on Wikidata is about books, articles
(including preprints), authors, organizations, journals, and publishers. These
items (corresponding to subject in Semantic Web parlance) can be interlinked
through Wikidata properties (corresponding to the predicate), such as author
(P50),13 published in (P1433), publisher (P123), series (P179), main theme
(P921), educated at (P69), employer (P108), part of (P361), sponsor (P859,
can be used for funding), cites (P2860) and several other properties.14
Numerous properties exist on Wikidata for deep linking to external resources,
e.g., for DOI, PMID, PMCID, arXiv, ORCID, Google Scholar, VIAF, Crossref
funder ID, ZooBank and Twitter. With these many identifiers, Wikidata can act
as a hub for scientometrics studies between resources. If no dedicated Wikidata
property exists for a resource, one of the URL properties can work as a substi-
tute for creating a deep link to a resource. For instance, P1325 (external data
available at) can point to raw or supplementary data associated with a paper.
We have used this scheme for scientific articles associated with datasets stored in
OpenfMRI [20], an online database with raw brain measurements, mostly from
functional magnetic resonance imaging studies. Using WDQS, we query the set
of OpenfMRI-linked items using the following query:
? item wdt : P1325 ? resource .
filter strstarts ( str (? resource ) ,
" https :// openfmri . org / dataset / " )
13
The URI for Wikidata property P50 is http://www.wikidata.org/prop/direct/P50
or with the conventional prefix wdt:P50. Similarly for any other Wikidata property.
14
A Wikidata table lists properties that are commonly used in bibliographic contexts:
https://www.wikidata.org/wiki/Template:Bibliographical_properties .
A similar scheme is used for a few of the scientific articles associated with data
in the neuroinformatics databases Neurosynth [26] and NeuroVault [6].
When bibliographic items exist in Wikidata, they can be used as references
to support claims (corresponding to triplets with extra qualifiers) in other items
of Wikidata, e.g., a biological claim can be linked to the Wikidata item for a
scientific journal.
By using these properties systematically according to an emerging data
model,15 editors have extended the bibliographic information in Wikidata. Par-
ticularly instrumental in this process was a set of tools built by Magnus Manske,
QuickStatements 16 and Source MetaData,17 including the latter’s associated Re-
solve authors tool.18 Information can be extracted from, e.g., PubMed, PubMed
Central and arXiv and added to Wikidata.
How complete is Wikidata in relation to scientific bibliographic information?
Journals and universities are well represented. For instance, 31,895 Wikidata
items are linked with the identifier for the Collections of the National Library of
Medicine (P1055). Far less covered are individual articles, individual researchers,
university departments and citations between scientific articles. Most of the sci-
entific articles in Wikidata are claimed to be an instance of (P31) the Wikidata
item scientific article (Q13442814). With a WDQS query, we can count the
number of Wikidata items linked to scientific article:
select ( count (? work ) as ? count ) where {
? work wdt : P31 wd : Q13442814 . }
As of 12 March 2017, the query returned the result 615,182, see also Table 1.
In comparison, arXiv states having 1,240,585 e-prints and ACM Digital Library
states to have 24,110 proceedings.19 There were 8,617 authors associated with
Wikidata items linked through the author property (P50) to items that are
instance of scientific article, and the number of citations as counted by triples
using the P2860 (cites) property stood at 2,729,164:
select ( count (? citedwork ) as ? count ) where {
? work wdt : P2860 ? citedwork . }
The completeness can be fairly uneven. Articles from PLOS journals are
much better represented than articles from the journals of IEEE.
The sponsor property has been used extensively for National Institute for
Occupational Safety and Health (NIOSH) with 52,852 works linking to the or-
ganization, 18,135 of which are instance of scientific articles, but apart from
NIOSH, the use of the property has been very limited for scientific articles.20
15
https://www.wikidata.org/wiki/Wikidata:WikiProject_Source_MetaData/
Bibliographic_metadata_for_scholarly_articles_in_Wikidata
16
https://tools.wmflabs.org/wikidata-todo/quick_statements.php
17
hhttps://tools.wmflabs.org/sourcemd/
18
https://tools.wmflabs.org/sourcemd/new_resolve_authors.php
19
As of 9 March 2017 according to https://arxiv.org/ and https://dl.acm.org/
contents_guide.cfm
20
National Institute for Occupational Safety and Health has a Wikimedian-in-
Residence program, through which James Hare has added many of the NIOSH works.
3 Scholia
Scholia provides both a Python package and
a Web service for presenting and interacting
with scientific information from Wikidata.
The code is available via https://github.
com/fnielsen/scholia, and a first release
has been archived in Zenodo [18]. As a Web
service, its canonical site runs from the Wiki-
media Foundation-provided service Wikime-
dia Tool Labs at https://tools.wmflabs.
org/scholia/, but the Scholia package may
be downloaded and run from a local server
as well. Scholia uses the Flask Python Web
framework [7]. The current Web service re-
lies entirely on Wikidata for all its presented
data. The frontend consists mostly of HTML
iframe elements for embedding the on-the-fly-
generated WDQS results and uses many of
the different output formats from this service:
tables, bubble charts, bar charts, line charts,
graphs and image lists.
Through a JavaScript-based query to the
MediaWiki API, an excerpt from the English
Wikipedia is shown on the top of each Scho-
lia page if the corresponding Wikidata item
is associated with an article in the English
Wikipedia. The label for the item is fetched
via Wikidata’s MediaWiki API. While some
other information can be fetched this way,
Scholia’s many aggregation queries are bet-
ter handled through SPARQL.
Scholia uses the Wikidata item identifier
as its identifier rather than author name,
journal titles, etc. A search field on the front Fig. 1. Overview screenshot of
page provides a Scholia user with the ability part of the Scholia Web page for an
to search for a name to retrieve the relevant author: https://tools.wmflabs.
Wikidata identifier. To display items, Scholia org/scholia/author/Q20980928.
sets up a number of what we call “aspects”. Fig. 2 zooms in on one panel.
The currently implemented aspects are au-
thor, work, organization, venue, series, publisher, sponsor and topic, see Table 2.
The present selection was motivated by the possibilities inherent in the Wikidata
items and properties. We plan to extend this to further aspects, e.g., award or
determination method. A URL scheme distinguishes the different aspects, so the
URL path /scholia/author/Q6365492 will show the author aspect of the statisti-
cian Kanti V. Mardia, while /scholia/topic/Q6365492 will show the topic aspect
Aspect Examples Example panels
Author Scientists List of publications, publications per year,
co-authors, topics, timelines, map, cita-
tions, academic tree.
Work Papers, books Recent citations, citations in the work,
statements supported in Wikidata
Organization Universities, research groups Affiliated authors, co-author graph, recent
publications, page production, co-author-
normalized citations per year
Venue Journals, proceedings Recent publications, topics in the publica-
tions, author images, prolific authors, most
cited works, most cited authors, most cited
venues
Series Proceedings series Items (venues) in the series, published
works from venues in the series
Publisher Commercial publisher Journals and other publications published,
associated editors, most cited papers, num-
ber of citations as a function of number of
published works
Sponsor Foundation List of publications funded, sponsored au-
thors, co-sponsors
Topic Keywords Recent publication on the topic, co-
occurring topics
Table 2. Aspects in Scholia: Each Wikidata item can be viewed in one or more aspects.
Each aspect displays multiple “panels”, which may be, e.g., a table of publications or
a bar chart of citations per year.
of the person, e.g., articles about Mardia. Likewise, universities can be viewed,
for instance, as organizations or as sponsors. Indeed, any Wikidata item can be
viewed in any Scholia aspect, but Scholia can show no data if the user selects a
“wrong” aspect, i.e. one for which no relevant data is available in Wikidata.
For each aspect, we make multiple WDQS queries based on the Wikidata
item for which the results in the panels are displayed, — technically in em-
bedded iframes. For the author aspect, Scholia queries WDQS for the list of
publications, showing the result in a table, displaying a bar chart of the number
of publications per year, number of pages per year, venue statistics, co-author
graph, topics of the published works (based on the “main theme” property), as-
sociated images, education and employment history as timelines, academic tree,
map with locations associated with the author, and citation statistics – see Fig. 1
for an example of part of an author aspect page. The citation statistics displays
the most cited work, citations by year and citing authors. For the academic tree
and the citation graph, we make use of Blazegraph’s graph analytics RDF GAS
Fig. 2. Screenshot of Scholia Web page with the number of papers published per year
for Finn Årup Nielsen: https://tools.wmflabs.org/scholia/author/Q20980928. In-
spired by LEGOLAS. Colors indicate author role: first, middle, last or solo author.
API21 that is available in WDQS. The embedded WDQS results link back to
WDQS where a user can modify the query. The interactive editor of WDQS
allows users not familiar with SPARQL to make simple modifications without
directly editing the SPARQL code.
Related to their work on quantifying conceptual novelty in the biomedical
literature [14], Shubhanshu Mishra and Vetle Torvik have set up a website pro-
filing authors in PubMed datasets: LEGOLAS.22 Among other information, the
website shows the number of articles per year, the number of citations per year,
the number of self-citations per year, unique collaborations per year and NIH
grants per year as bar charts that are color-coded according to, e.g., author role
(first, solo, middle or last author). Scholia uses WDQS for LEGOLAS-like plots.
Figure 2 displays one such example for the number of published items as a func-
tion of year of publication on an author aspect page, where the components of
the bars are color-coded according to author role.
For the organization aspect, Scholia uses the employer and affiliated Wiki-
data properties to identify associated authors, and combines this with the au-
thor query for works. Scholia formulates SPARQL queries with property paths to
identify suborganizations of the queried organization, such that authors affiliated
with a suborganization are associated with the queried organization. Figure 3
shows a corresponding bar chart, again inspired by the LEGOLAS style. Here,
the Cognitive Systems section at the Technical University of Denmark is dis-
played with the organization aspect. It combines work and author data. The
21
https://wiki.blazegraph.com/wiki/index.php/RDF_GAS_API
22
http://abel.lis.illinois.edu/legolas/
Fig. 3. Scholia screenshot with page production for a research section (Cognitive Sys-
tems at the Technical University of Denmark), where the number of pages per paper
has been normalized by the number of authors. The bars are color-coded according to
author. The plot is heavily biased, as only a very limited subset of papers from the
section is available in Wikidata, and the property for the number of pages is set for only
a subset of these papers. From https://tools.wmflabs.org/scholia/organization/
Q24283660.
bar chart uses the P1104 (number of pages) Wikidata property together with a
normalization based on the number of authors on each of the work items. The
bars are color-coded according to individual authors associated with the organi-
zation. In this case, the plot is heavily biased, as only a very limited subset of
publications from the organization is currently present in Wikidata, and even the
available publications may not have the P1104 property set. Other panels shown
in the organization aspect are a co-author graph, a list of recent publications
formatted in a table, a bubble chart with most cited papers with affiliated first
author and a bar chart with co-author-normalized citations per year. This last
panel counts the number of citations to each work and divides it by the number
of authors on the cited work, then groups the publications according to year and
color-codes the bars according to author.
For the publisher aspect, Scholia queries all items where the P123 property
(publisher) has been set. With these items at hand, Scholia can create lists
of venues (journals or proceedings) ordered according to the number of works
(papers) published in each of them, as well as lists of works ordered according
to citations. Fig. 4 shows an example of a panel on the publisher aspect page
with a scatter plot detailing journals from BioMed Central. The position of each
journal in the plot reveals impact factor-like information.
For the work aspect, Scholia lists citations and produces a partial citation
graph. Fig. 5 shows a screenshot of the citation graph panel from the work
Fig. 4. Screenshot from Scholia’s publisher aspect with number of publications versus
number of citations for works published by BioMed Central. The upper right point
with many citations and many published works is the journal Genome Biology. From
https://tools.wmflabs.org/scholia/publisher/Q463494.
aspect for a specific article [3]. For this aspect, we also formulate a special query
to return a table with a list of Wikidata items where the given work is used
as a source for claims. An example query for a specific work is shown with
Listing 1. From the query results, it can be seen, for instance, that the article
A novel family of mammalian taste receptors [1] supports a claim about Taste
2 receptor member 16 (Q7669366) being present in the cell component (P681)
integral component of membrane (Q14327652). For the topic aspect, Scholia uses
a property path SPARQL query to identify subtopics. For a given item where
the aspect is not known in advance, Scholia tries to guess the relevant aspect
by looking at the instance of property. The Scholia Web service uses that guess
for redirecting, so /scholia/Q8219 will redirect to /scholia/author/Q8219, the
author aspect for the psychologist Uta Frith. This is achieved by first making
a server site query to establish that Uta Frith is a human and then using that
information to choose the author aspect as the most relevant aspect to show
information about Uta Frith.
A few redirects for external identifiers are also implemented. For instance,
with Uta Frith’s Twitter name ‘utafrith’, /scholia/twitter/utafrith will redirect
Listing 1. SPARQL query on the work aspect page for claims supported by a work,
— in this case Q22253877 [1].
SELECT distinct ? item ? itemLabel ? property ? propertyLabel
? value ? valueLabel WHERE {
? item ? p ? statement .
? property wikibase : claim ? p .
? statement ? a ? value .
? item ? b ? value .
? statement prov : wasDerivedFrom /
< http :// www . wikidata . org / prop / reference / P248 >
wd : Q22253877 .
SERVICE wikibase : label {
bd : serviceParam wikibase : language " en " }
} ORDER BY ? itemLabel
to /scholia/Q8219, which in turn will redirect to /scholia/author/Q8219. Scholia
implements similar functionality for DOI, ORCID and GitHub user identifier.
4 Using Wikidata as a bibliographic resource
As a command-line tool, Scholia provides a prototype tool that uses Wikidata
and its bibliographic data in a LATEX and BibTEX environment. The current
implementation looks up citations in the latex-generated .aux file and queries
Wikidata’s MediaWiki API to get cited Wikidata items. The retrieved items are
formatted and written to a .bib that bibtex can use to format the bibliographic
items for inclusion in the LATEX document. The workflow for a LATEX document
with the filename example.tex is
latex example
python -m scholia . tex write - bib - from - aux example . aux
bibtex example
latex example
latex example
Here, the example document could read
\ documentclass { article }
\ usepackage [ utf8 ]{ inputenc }
\ begin { document }
\ cite { Q18507561 }
\ bibliographystyle { plain }
\ bibliography { example }
\ end { document }
In this case, the \cite command cites Q18507561 (Wikidata: a free col-
laborative knowledgebase [25]). A DOI can also be used in the \cite command:
instead of writing \cite{Q18507561}, one may write \cite{10.1145/2629489}
Fig. 5. Screenshot of part of a Scholia Web page at https://tools.wmflabs.org/
scholia/work/Q21143764 with the partial citation graph panel of the work aspect for
Johan Bollen’s article from 2009 [3].
to get the same citation. Scholia matches on the “10.” DOI prefix and makes a
SPARQL query to get the relevant Wikidata item.
The scheme presented above can take advantage of the many available style
files of BibTEX to format the bibliographic items in the various ways requested
by publishers. We have used Scholia for reference management in this paper.
5 Discussion
WDQS and Scholia can provide many different scientometrics views of the data
available in Wikidata. The bibliographic data in Wikidata are still quite limited,
but the number of scientometrically relevant items will likely continue to grow
considerably in the coming months and years.
The continued growth of science data on Wikidata can have negative impact
on Scholia, making the on-the-fly queries too resource demanding. In the current
version, there are already a few queries that run into WDQS’s time out, e.g.,
it happens for the view of co-author-normalized citations per year for Harvard
University. If this becomes a general problem, we will need to redefine the queries.
Indeed, the WDQS time out will be a general problem if we want to perform
large-scale scientometrics studies. An alternative to using live queries would be
using dumps, which are available in several formats on a weekly basis, with
daily increments in between.23 The problem is not a limitation of SPARQL,
but a limitation set by the server resources. Some queries may be optimized,
especially around the item labeling.
Working with Scholia has made us aware of several issues. Some of these
are minor limitations in the Wikidata and WDQS systems. The Wikidata label
length is limited to 250 characters, whereas the ‘monolingual text’ datatype used
for the ‘title’ property (P1476) is limited to 400 characters. There are scholarly
articles with titles longer than those limits.
Wikidata fields cannot directly handle subscripts and superscripts, which
commonly appear in titles of articles about chemical compounds, elementary
particles or mathematical formulas. Other formatting in titles cannot directly
be handled in Wikidata’s title property,24 and recording a date such as “Summer
2011” is difficult.
Title and names of items can change. Authors can change names, e.g. due to
marriage, and journals can change titles, e.g. due to a change of scope or transfer
of ownership. For instance, the Journal of the Association for Information Sci-
ence and Technology has changed names several times over the years.25 Wikidata
can handle multiple titles in a single Wikidata item and with qualifiers describe
the dates of changes in title. For scientometrics, this ability is an advantage in
principle, but multiple titles can make it cumbersome to handle when Wikidata
is used as a bibliographic resource in document preparation, particularly for ar-
ticles published near the time when the journal changed its name. One way to
alleviate this problem would be to split the journal’s Wikidata item into several,
but this is not current practice.
In Wikidata, papers are usually not described to be affiliated with organi-
zations. Scholia’s ability to make statistics on scientific articles published by
organization is facilitated by the fact that items about scientific articles can
link to items about authors, which can link to items about organizations. It is
possible to link scientific articles to organization by using Wikidata qualifiers
in connection with the author property. However, this scheme is currently in
limited use.
This scarcity of direct affiliation annotation on Wikidata items about articles
means that scientometrics on the organizational level are unlikely to be precise
at present. In the current version, Scholia even ignores any temporal qualifier for
23
https://www.wikidata.org/wiki/Wikidata:Database_download
24
By way of an example, consider the article “A library of 7TM receptor C-terminal
tails. Interactions with the proposed post-endocytic sorting proteins ERM-binding
phosphoprotein 50 (EBP50), N-ethylmaleimide-sensitive factor (NSF), sorting nexin
1 (SNX1), and G protein-coupled receptor-associated sorting protein (GASP)”, an
article with the title “Cerebral 5-HT2A receptor binding is increased in patients with
Tourette’s syndrome”, where “2A” is subscripted and “User’s Guide to the amsrefs
Package”, where the “amsrefs” is set in monospaced font.
25
http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2330-1643/issues
records these former titles: Journal of the American Society for Information Science
and Technology, Journal of the American Society for Information Science, and
American Documentation.
the affiliation and employer property, meaning that a researcher moving between
several organization gets his/her articles counted under multiple organizations.
Data modeling on Wikidata gives rise to reflections on what precisely a “pub-
lisher” and a “work” is. A user can set the publisher Wikidata property of a
work to a corporate group, a subsidiary or possibly an imprint. For instance,
how should we handle Springer Nature, BioMed Central and Humana Press?
Functional Requirements for Bibliographic Records (FRBR) [11] suggests a
scheme for works, expressions, manifestations and “items”. In Wikipedia, most
items are described on the work level as opposed to the manifestation level (e.g.,
book edition), while citations should usually go to the manifestation level. How
should one deal with scientific articles that have slightly different “manifesta-
tions”, such as preprint, electronic journal edition, paper edition and postprint,
or editorials that were co-published in multiple journals with identical texts? An
electronic and a paper edition may differ in their dates of publication, but oth-
erwise have the same bibliographic data, while a preprint and its journal edition
usually have different identifiers and may also differ in content. From a sciento-
metrics point of view, these difference in manifestation may not matter in some
cases, but could be the focus of others. Splitting a scientific article as a work
(in the FRBR sense) over multiple Wikidata items seems only to complicate
matters.
The initial idea for Scholia was to create a researcher profile based on Wiki-
data data with list of publications, picture and CV-like information. The in-
spiration came from a blog post by Lambert Heller: What will the scholarly
profile page of the future look like? Provision of metadata is enabling experimen-
tation.26 In this blog post, he discussed the different features of several scholarly
Web services: ORCID, ResearchGate, Mendeley, Pure, VIVO, Google Scholar
and ImpactStory. In Table 3, we have set up a table listing Heller’s features
for the Wikidata–Scholia combination. Wikidata–Scholia performs well in most
aspects, but in the current version, Scholia has no backend for storing user data,
and user features such as forum, Q&A and followers are not available.
Beyond the features listed by Heller, which features set Wikidata–Scholia
apart from other scholarly Web services? The collaborative nature of Wikidata
means that Wikidata users can create items for authors that do not have an
account on Wikidata. In most other systems, the researcher as a user of the
system has control over his/her scholarly profile and other researchers/users
cannot make amendment or corrections. Likewise, when one user changes an
existing item, this change will be reflected in subsequent live queries of that
item, and it may still be in future dumps if not reverted or otherwise modified
before the dump creation.
With WDQS queries, Scholia can combine data from different types of items
in Wikidata in a way that is not usually possible with other scholarly profile Web
services. For instance, Scholia generates lists of publications for an organization
by combining items for works and authors and can show co-author graphs re-
26
http://blogs.lse.ac.uk/impactofsocialsciences/2015/07/16/
scholarly-profile-of-the-future/
Feature Description
Business model Y Community donations and funding from foundations
to Wikimedia Foundation and affiliated chapters
Portrait picture Y The P18 property can record Wikimedia Commons
images related to a researcher
Alternative names Y Aliases for all items, not just researchers
IDs / profiles in other sys- Y Numerous links to external identifiers: ORCID, Sco-
tems pus, Google Scholar, etc.
Papers and similar Y Papers and books are individual Wikidata items
Uncommon research prod- Y For instance, software can be associated with a de-
ucts veloper
Grants, third party funding (N) Currently no property for grant holders and proba-
bly no individual grants in Wikidata. The sponsor
property can be used to indicate the funding of a
paper
Current institution Y Affiliation and employer can be recorded in Wikidata
Former employers, educa- Y Education, academic degree can be specified, and for-
tion mer employers can be set by way of qualifiers
Self-assigned keywords (Y) The main theme of a work can be specified, interests
or field of work can be set for a person. The values
must be items in Wikidata. Users can create items.
Concepts from controlled Y See above
vocabulary
Social graph of follower- N There are no user accounts on the current version of
s/friends Scholia.
Social graph of coauthors Y
Citation/attention meta- Y Citations between scientific articles are recorded with
data from platform itself a property that can be used to count citations. Cita-
tion/reference between Wikidata items.
Citation/attention meta- (N) Deep links to other citation resources like Google
data from other source Scholar and Scopus.
Comprehensive search to (N) Several tools liks Magnus Manske’s Source MetaData
match/include papers that look up bibliographic metadata based on DOI,
PMID or PMCID
Forums, Q&A etc. N
Deposit own papers (Y) Appropriately licensed papers can be uploaded to
Wikimedia Commons or Wikisource
Research administration N
tools
Reuse of data from outside Y API, WDQS, XML dump, third-party services
of the service
Table 3. Overview of Wikidata and Scholia features in terms of a scholarly profile.
Directly inspired by a blog post by Lambert Heller (see text).
stricted by affiliation. Similarly, the co-author graph can be restricted to authors
publishing works annotated with a specific main theme. Authors are typically
annotated with gender in Wikidata, so Scholia can show gender color-coding of
co-author graphs. On the topic aspect page, the Scholia panel that shows the
most cited works that are cited from works around the topic can point to an
important paper for a topic – even if the paper has not been annotated with
the topic – by combining the citations data and topic annotation. References for
claims are an important part of Wikidata and also singles Wikidata out among
other scholarly profile Web service, and it acts as an extra scientometrics dimen-
sion. The current version of Scholia has only a single panel where the query uses
references: the “Supports the following statement(s)” on the work aspect page,
but it is possible to extend the use of this scientometrics dimension.
6 Acknowledgements
This work was supported by Innovationsfonden through the DABAI project.
The work on Scholia was spawned by the WikiCite project [23]. We would like to
thank the organizers of the workshop, particular Dario Taraborelli. Finn Årup
Nielsen’s participation in the workshop was sponsored by an award from the
Reinholdt W. Jorck og Hustrus Fund. We would also like to thank Magnus
Manske and James Hare for considerable work with Wikidata tools and data in
the context of WikiCite.
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