=Paper=
{{Paper
|id=Vol-2275/short1
|storemode=property
|title=Wikidata as an intuitive resource towards semantic data modeling in data FAIRification
|pdfUrl=https://ceur-ws.org/Vol-2275/short1.pdf
|volume=Vol-2275
|authors=Annika Jacobsen,Andra Waagmeester,Rajaram Kaliyaperumal,Greg Stupp,Lynn Schriml,Mark Thompson,Andrew I. Su,Marco Roos
|dblpUrl=https://dblp.org/rec/conf/swat4ls/JacobsenWKSS0SR18
}}
==Wikidata as an intuitive resource towards semantic data modeling in data FAIRification==
https://ceur-ws.org/Vol-2275/short1.pdf
Wikidata as an intuitive resource towards
semantic data modeling in data FAIRification
Annika Jacobsen1[0000−0003−4818−2360] , Andra
Waagmeester2[0000−0001−9773−4008] , Rajaram
1[0000−0002−1215−167X]
Kaliyaperumal , Gregory S. Stupp3[0000−0002−0644−7212] ,
4[0000−0001−8910−9851]
Lynn M. Schriml , Mark Thompson1[0000−0002−7633−1442] ,
3[0000−0002−9859−4104]
Andrew I. Su , and Marco Roos1[0000−0002−8691−772X]
1
Department of Human Genetics, Leiden University Medical Center, Leiden, The
Netherlands
2
Micelio, Antwerp - Ekeren, Belgium
3
The Scripps Research Institute, San Diego CA 92037, USA
4
University of Maryland School of Medicine, Baltimore, MD, USA
Abstract. Data with a comprehensible structure and context is easier
to reuse and integrate with other data. The guidelines for FAIR (Find-
able, Accessible, Interoperable, Reusable) data for humans and comput-
ers provide handles to transform data existing in silos into well connected
knowledge graphs (linked data). Semantic data models are key in this
transformation and describe the logical structure of the data and the rela-
tionships between the data entities. This description is provided through
IRIs (Internationalized Resource Identifiers) which link to existing on-
tologies and controlled vocabularies. Creating a semantic data model is
a labour-intensive process, which requires a solid understanding of the
selected domains and the applicable ontologies. Moreover, in order to
achieve a useful degree of Interoperability between datasets, either the
datasets need to use the same (set of) ontologies, or the ontologies them-
selves need to be aligned and mapped. The former requires implementa-
tion of extensive (social) processes to achieve consensus, while the latter
requires relatively advanced semantic engineering. We argue that this
poses a significant obstacle for (otherwise capable) novice data modelers
and even experienced data stewards.
Here, we propose that Wikidata can be used as an intuitive re-
source for resolvable IRIs both for teaching and studying semantic data
modeling. In this way Wikidata serves as a hub in the linked data cloud
connecting different but similar ontologies. We elaborate current prob-
lems and how Wikidata can be used to tackle these. As an example we
describe two genetic variant models, one generated in a workshop and
one generated using Wikidata. This shows how Wikidata can be instru-
mental in mapping similar concepts in different ontologies in a way that
can benefitFAIR data stewardship processes in education and research.
Keywords: semantic modeling · FAIR · Wikidata · ontology · concept
mapping
2 Annika Jacobsen et al.
1 Opportunistic semantic data modeling
Data integration between heterogeneous data sets can be enabled by making
them machine-readable, which formally captures their structure and context.
One common way of generating such linked data is by using the combination of
Resource Description Framework (RDF) triples and Internationalized Resource
Identifiers (IRIs), the latter providing semantics to the data. In addition it is cru-
cial that the context is made explicit through IRIs. This orchestration between
IRIs can be captured in a semantic data model. Generating linked, interoperable
data by using semantic modeling is central to making data FAIR (Findable, Ac-
cessible, Interoperable, and Reusable) for humans and computers [1]. However,
creating a semantic data model is a laborious process. This process, which re-
quires expertise both in the field under scrutiny and in ontologies and controlled
vocabularies, is coordinated by a data steward. Further, given the relative nov-
elty of the field, the availability of data stewards does not scale to the demand
in the life sciences field.
To disseminate expertise of FAIRification, so-called Bring Your Own Data
(BYOD) workshops have been conducted for the last five years, where FAIR
and domain experts work together to FAIRify heterogeneous data [2]. A large
part of this process consists of creating the underlying semantic data model. An
example of such a model, generated in a BYOD held 6-8 June 2017 in Utrecht,
The Netherlands [3, 4], can be seen in Figure S1. This model describes data
that reflect measurements in samples from whole genome sequencing experi-
ments, available in a variety of non-linked data formats. These models tend to
be rather opportunistic in their onset, because the BYOD participants typically
have diverse backgrounds. The different ontologies and controlled vocabularies
are often cherry-picked based on the respective preferences of the participants
and experts. Different resources exist to semantically express the same data even
within the same domain: for example, OBO [5], Bioschemas [6] and SIO [7] serve
partially overlapping and partially distinct areas of semantics in the life sciences.
Therefore, initial BYOD models often use multiple namespaces, because it is dif-
ficult to dictate a clear guideline to select one single source for semantics. This is
demonstrated by the large number of results per term in several state-of-the-art
ontology search tools (Table S1). Even if the model was harmonized on a small
set of ontologies and controlled vocabularies, the numbers mentioned in Table
S1 suggest that different data modeling groups still would end up using different
harmonized sets. This raises the question on how interoperable and reusable the
resulting linked, FAIR data really is. For linked data that uses distinct sets of
ontologies and vocabularies to be interoperable, it is essential to have mappings
between their vocabulary terms and ontological concepts, otherwise the resulting
linked data effectively remains a data silo.
In this paper, we propose that Wikidata [8] may be used as a source for IRIs
and serve as a potential hub linking different opportunistic semantic data models
both for education and research. Wikidata is a linked database contributed by
both humans and machines. We first, describe an opportunistic semantic data
Title Suppressed Due to Excessive Length 3
model of genetic variants generated in a BYOD and show how Wikidata can be
used for data model construction and ontology mappings.
2 Semantic Data modeling with Wikidata IRIs
Wikidata is a linked database and a sister Wikimedia project of Wikipedia [8].
What Wikipedia is to text, Wikidata is to data: anyone, both humans and ma-
chines, can contribute to Wikidata as long as its primary source of the contri-
bution is available under a public license. Wikidata has an RDF representation
using Wikidata namespaces, which enables Wikidata concepts (items) to be em-
bedded into RDF knowledge graphs. Issuing Wikidata items and statement val-
ues are open to all. On the other hand, properties link items in the Wikidata
namespace (e.g. molecular function in Figure S2) are predefined and new prop-
erties need to go through a proposal process before they can be instantiated.
Although Wikidata is not limited to a constrained set of domains, there are var-
ious active initiatives in the Biomedical domain to synchronize Wikidata with
knowledge from authoritative biomedical resources [9, 10], such as the Disease
Ontology [11] or Gene Ontology [12], where respective items have mappings to
the original ontologies. Instead of having to sort through a wide variety of sug-
gestions provided by the different ontology search tools, Wikidata can act as a
single entry point for IRIs to create a semantic data model for data FAIRifica-
tion. Wikidata provides three different ways that make it a viable source for IRIs
to be used in initial steps of transforming unstructured research data into FAIR
data. Firstly, the various (language) labels and descriptions associated with an
item may be modified or extended by Wikidata users to enrich the definition
of an item. The direct link to related wikipedia articles helps to disambiguate
items so as not to (unintentionally) change the intended semantics of an item.
Secondly, one could use the wikidata items with mappings to existing ontolo-
gies. Finally, one could choose to mint a new Wikidata item to reflect a specific
concept that does not exist yet as a wikidata item.
Using Wikidata properties and items we expressed a semantic data model
for genetic variants (see Figure 1). This is the same use case as illustrated in
Figure S1, but here only Wikidata was used as a controlled vocabulary, and we
added the identified IRIs from the used ontologies as mappings to this wiki-
data model, thus increasing the potential for semantic interoperability. Creating
mappings was easily achieved using the Wikidata community edit interface by
attaching a wikidata exact match property to the relevant items to connect them
to external ontology IRIs. If one group’s opportunistic model used for example
an OBO ontology instead of the SIO ontology used by a second group, then
data integration may be challenging. However, since both of those terms can be
reconciled through Wikidata using the available mapping properties, the intro-
duction of Wikidata facilitates automated or semi-automated harmonization of
independently-authored opportunistic models.
4 Annika Jacobsen et al.
Fig. 1. Semantic data model of a genetic variant using Wikidata.
3 Conclusion and future work
In this paper, we put forward the position that Wikidata is a well-suited con-
trolled vocabulary for data FAIRification in the life sciences, especially in initial
stages, where it is either too early to adhere to specific domain descriptions,
or specific data or project constraints and requirements are yet to emerge. As
said, this leads to opportunistic data models where parts of different ontologies
are cherry picked. By using Wikidata namespaces, this decision can be post-
poned or (partially) obviated. Once applicable ontologies become apparent, one
could update Wikidata with mappings, thus linking the Wikidata namespace
to external ones. For many data managers and data stewards, linked data ap-
proaches and technologies have a relatively steep learning curve and dealing with
the wide variety of available ontologies is a major contributing factor. We argue
that Wikidata is a good starting point to create initial models while maintain-
ing the flexibility to evolve those models in the future. Additionally, it is easy
to add new concepts and to do mappings using Wikidata so that resolvable IRIs
Title Suppressed Due to Excessive Length 5
are instantly available for representing data as linked data. In comparison, ex-
tending other commonly used ontologies requires engaging with their curators
or maintainers, which is not always possible or easy.
In conclusion, we argue that Wikidata is a viable source for IRIs in the process
making data FAIR. Wikidata is open to everyone to add terms, properties and
mappings to external ontologies. This together with the fact that every Wikidata
items has a resolvable IRI, makes data using Wikidata items as IRI and its
properties - also with IRIs - interoperable. Wikidata is useful resource in any
FAIRification process. As part of future work, we would like to investigate how
different semantic data models representing the same data compare to each other,
what their respective limitations are and how Wikidata can be used to map from
one to the other in a more extensive interoperability use case. We plan to do
such a modelling exercise in the foreseeable future and welcome collaborations
in doing so.
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