=Paper=
{{Paper
|id=Vol-3127/paper-5
|storemode=property
|title=The Use of Foundational Ontologies in Bioinformatics
|pdfUrl=https://ceur-ws.org/Vol-3127/paper-5.pdf
|volume=Vol-3127
|dblpUrl=https://dblp.org/rec/conf/swat4ls/BernabeQSSJMR22
}}
==The Use of Foundational Ontologies in Bioinformatics==
https://ceur-ws.org/Vol-3127/paper-5.pdf
The use of Foundational Ontologies in
Bioinformatics?
César Henrique Bernabé1[0000−0003−1795−5930] , Núria
Queralt-Rosinach1[0000−0003−0169−8159] , Vı́tor E. Silva
Souza3[0000−0003−1869−5704] , Luiz Olavo Bonino da Silva
1,2[0000−0002−1164−1351]
Santos , Annika Jacobsen1[0000−0003−4818−2360] , Barend
1[0000−0003−3934−0072]
Mons , and Marco Roos1[0000−0002−8691−772X]
1
Leiden University Medical Center, Leiden, The Netherlands
{c.h.bernabe, n.queralt rosinach, a.jacobsen, b.mons, m.roos}@lumc.nl
2
University of Twente, Enschede, The Netherlands
l.o.boninodasilvasantos@utwente.nl
3
Federal University of Espı́rito Santo
vitor.souza@ufes.br
Abstract. Ontologies have been used in biomedicine for several pur-
poses, such as knowledge representation, data analysis and integration.
The FAIR principles recommend the use of controlled vocabularies, such
as ontologies, to define data terms precisely. However, ontologies are
currently modelled following different approaches, sometimes defining
overlapping concepts with conflicting definitions, which can harm data
FAIRness. Foundational ontologies are defined as domain-independent
ontologies that describe the most basic concepts of our world, and thus
used to lessen the interoperability problem. With the aim to investigate
how foundational ontologies can improve the benefits of FAIR data for
life sciences research, we observed a need to assess how they are used
in the area. To support our investigation, we conducted a systematic
literature analysis, in which we selected appropriate works according to
predefined criteria. From the selected works, we identified that, besides
being used for several purposes, there is almost no empirical evidence
testing the claims for or against the use of foundational ontologies. This
indicates a need for evaluating the use of such artefacts in bioinformatics.
Similarly, we observed a low adherence to formal ontology construction
and evaluation methods during ontologies development, which can im-
pact the quality and sustainability of ontologies, and thus the FAIRness
of ontologized data.
Keywords: Systematic Literature Mapping · Foundational Ontologies
· FAIR · Biomedical Ontologies · Ontology Engineering · Ontology Eval-
uation · Ontology Application.
?
This initiative has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement N°825575 and the Trusted World
of Corona (TWOC; LSH Health Holland).
Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 C. Bernabé et al.
1 Introduction
The biomedical field is facing an increasing growth in the volume of research
data, which is impossible to analyse by human agents alone. To cope with this,
several approaches have been proposed to make data and metadata (i.e. descrip-
tion of data) machine-readable and -actionable, such as to enable computers to
understand and automatically process it. The FAIR principles [25] aim to make
data Findable, Accessible, Interoperable and Reusable for both humans and ma-
chines, thus enabling efficient data analysis across multiple resources with min-
imal human intervention. The FAIR principles recommend the use of controlled
vocabularies (e.g., ontologies) in the description of metadata and data terms,
seeking to achieve an unambiguous understanding of concepts and properties.
In life sciences research, ontologies are used to support knowledge man-
agement and to improve data analysis, shareability and interoperability [14].
However, ontologies are currently modelled in different paradigms, sometimes
describing overlapping concepts with conflicting definitions. Hence, there is a
risk for the interoperability problem to happen to ontologies just as it does to
data [13]. Moreover, misalignment between ontologies can impact the reliability
of data analysis results due to the misunderstanding of terms, and also impact
the (meta)data adherence to FAIR principles (FAIRness) [8].
Foundational ontologies are one of the approaches proposed to improve on-
tology interoperability, since they provide a general and domain-independent
conceptual architecture that describes the most basic kinds of entities and rela-
tionships. Consequently, foundational ontologies support a more precise descrip-
tions of the concepts and relations in a given universe of discourse [8].
We investigate how foundational ontologies are used in bioinformatics, what
advantages they claim to have, and if these are validated for the biomedical
domain, as well as what the gaps and opportunities are in this particular area
of research. Our approach is based on a Systematic Literature Mapping (SLM),
which is a method to analyse the state of the art on a particular topic [12].
To our knowledge, no other similar SLM has been conducted on this research
question. Our overarching goal is that our findings will guide us to define how
foundational ontologies can be used to improve biomedical data FAIRness.
2 Ontologies and their Abstraction Levels
Several definitions of the term “ontology” have been proposed in the last decades.
Studer, Benjamins & Fensel [23] define ontologies as a “formal, explicit specifica-
tion of a shared conceptualization”. ‘Formal’ means that the model is logically
defined so it supports algorithmic reasoning. ‘Explicit’ refers to concepts be-
ing defined with unambiguous descriptions. Finally, ‘shared conceptualization’
refers to the consensual definition of domain concepts within the community of
expected users.
Ontologies can be modelled in different levels of detail, and classified accord-
ingly [7]. Application ontologies are built to address a specific use case, usually
The use of Foundational Ontologies in Bioinformatics 3
constrained to a particular activity (e.g. orchestrate a machine learning work-
flow). Domain ontologies describe concepts related to a domain of discourse (e.g.,
rare diseases). Core ontologies provide an upper-level structural definition to a
field that spans across different domains (e.g. biomedicine). Foundational ontolo-
gies define very general and domain-independent concepts (e.g., time, event and
object). In the literature, foundational ontologies are also defined as “top” or
“upper-level” ontologies, while core ontologies can also be described as “domain
upper ontologies” or “middle-level ontologies”.
The Basic Formal Ontology (BFO) [5], the Descriptive Ontology for Lin-
guistic and Cognitive Engineering (DOLCE) [15], the General Formal Ontology
(GFO) [9] and the Suggested Upper Merged Ontology (SUMO) [16] are exam-
ples of foundational ontologies, while Biotop [2] is an example of core ontology.
Currently, most biological and biomedical ontologies are registered in reposito-
ries such as the NCBO Bioportal [18] and the Open Biological and Biomedical
Ontologies (OBO) Foundry [22].
3 Systematic Literature Mapping Method and Output
The SLM research method used for our literature study is defined by Kitchen-
ham [12] as a secondary study designed to answer broad questions about a
research area. Planning, Conducting and Reporting are the main steps of an
SLM process, which are further divided into more specific tasks. In the planning
step, the research questions, research sources, research query and selection cri-
teria are defined. In the conducting step, papers are extracted from considered
sources, deduplicated and selected according to inclusion and exclusion rules. In
the reporting step, the results from the SLM are compiled and discussed in the
form of answers to research questions.
Planning. Based on the literature (e.g., [11]), we observed that, despite be-
ing used for several purposes in bioinformatics, there are very few experiments
testing the claims and drawbacks of foundational ontologies in the field. Ad-
ditionally, a second and complementary observation is that there is a lack of
methodological rigour in the development and evaluation of ontologies in bioin-
formatics. Consequently, we defined five research questions in the first task of the
planning step. First of all, we would like to know “How are foundational ontolo-
gies used in bioinformatics?” (RQ1 ). Secondly, we would like to investigate the
reason why foundational ontologies are or are not used, and hence we ask two
questions: “What are the claimed advantages of using foundational ontologies?”
(RQ2 ) and “What are the claimed drawbacks of using foundational ontologies?”
(RQ3 ). Third, since the answers to RQ2 and RQ3 are based on perceptions
by the extracted papers’ authors, we would like to find scientific support for
the answers to the questions by asking “What is the empirical evidence for the
advantages and drawbacks?” (RQ4 ). Finally, our second observation could be
answered by asking “From the total number of papers that describe the develop-
ment of a biomedical ontology, how many use existing formal development and
evaluation methods?” (RQ5 ).
4 C. Bernabé et al.
Based on our research questions, we selected papers that apply a foundational
ontology in life science domains (Inclusion Criteria - IC). We excluded papers
that did use a foundational ontology but are not related to life sciences (Exclusion
Criteria 1 - EC1). For the sake of reproducibility of this study, we also excluded
papers not written in English (EC2).
Sources were selected considering the biological and computational aspects
of bioinformatics. We included one biosemantics focused source (Jane4 ), one
biomedical (Pubmed5 ) and a third that also covers areas from computer sci-
ence (Science Direct6 ). Finally, the search strategy was driven by the fact that
different terms are used to describe foundational ontologies, as mentioned in
Section 2. Thus, we included the “top level” and “upper level” synonyms in the
search string, which can be generically described as: (”foundational ontology”
OR ”top-level ontology” OR ”top level ontology” OR ”upper-level ontology” OR
”upper level ontology” OR ”upper ontology”) AND (”biology” OR ”biomedical”
OR ”biomedicine” OR ”biological”). The specific search strings, the search re-
sults (and the date each one was performed), are available in the supplementary
material at https://doi.org/10.5281/zenodo.5793618.
Conducting. In the extraction process, the search string was used in the
mentioned sources and applied to the paper’s full text. The search result was
downloaded from each source, merged, deduplicated and selected according to
the criteria defined. The selection process is performed in three steps, where
papers are firstly selected based on information from (i) the title and abstract.
Then, the results from the first step are reanalysed, now by performing (ii)
diagonal reading (title, abstract, introduction and conclusion sections, figures
and tables). In the third step, papers from step two are finally selected/excluded
based on (iii) full-text reading.
Reporting. In this final step, the analysis conducted on the resulting selec-
tion of papers is compiled and reported. During the final iteration of the selection
process, the individual answers were manually annotated on a reading sheet and
then synthesized in mind maps. Due to space constraints, the mind maps and
the information about the extraction process (which criteria was applied to each
paper in each phase) are available as supplementary material.
4 Data Synthesis
The first step of the extraction process resulted in 404 records, which were then
deduplicated, resulting in 346 papers. The final step of the extraction process
resulted in 51 papers.
Figure 1a presents the number of selected papers per year of publication. The
set comprehend works published from 2004 to 2019, with an average of 2 papers
published per year from 2004 to 2010 and 4 papers published per year from 2011
to 2019, with a peak of 6 papers published in 2011, 2012 and 2015. Figure 1b
4
jane.biosemantics.org/
5
pubmed.ncbi.nlm.nih.gov/
6
sciencedirect.com/
The use of Foundational Ontologies in Bioinformatics 5
shows the foundational ontologies popularity between the set of selected papers.
It can be noticed that BFO is the most used foundational ontology, followed
by DOLCE, GFO and SUMO, respectively. Figure 2 compares the use of the
foundational ontologies per year (some works used more than one ontology). We
consider that a foundational ontology is “used” when it is applied to one of the
activities described in the answer to RQ1 (cf. Section 4.1).
(a) Number of publications per year among the (b) Popularity of each founda-
set of selected papers in the SLM. tional ontology among the set of
selected papers in the SLM.
Fig. 1: Overview of selected papers: number of publications per year (a) and
number of uses of each foundational ontology (b).
Fig. 2: The number of uses of each foundational ontology (Y axis) per year (X
axis) among the set of selected papers in the SLM.
4.1 Synthesized Responses to Research Questions
During data analysis, periodic meetings between co-authors were conducted to
discuss the information extraction process in an attempt to reduce possible bias
in our synthesis.
6 C. Bernabé et al.
Answers to RQ1 can be classified into five main categories:
– Ontology development: in most cases, foundational ontologies were used
as a starting point for ontology design, providing a set of basic categories in
a top-down development strategy, where classes from the selected founda-
tional ontology are used as a reference for deriving domain concepts. Founda-
tional ontologies were also used in bottom-up (existing domain concepts are
anchored in foundational ones), or middle-out (hybrid) approaches. Founda-
tional ontologies also supported the development of ontology design patterns.
– Ontology enrichment: the axioms provided by foundational ontologies
were added to domain concepts, thus improving inference and supporting
the identification of new knowledge.
– Ontology merging or alignment: foundational ontologies were used as
a common ground for the process of ontology merging, where different do-
main ontologies are combined to produce a new one. Similarly, foundational
ontologies were used to develop mappings between different core/domain
ontologies, usually with the aim of interoperability improvement.
– Ontological analysis: the ontological commitments (categories, relation-
ships, constraints and axioms) defined by foundational ontologies were used
to identify and repair inconsistencies in domain ontologies or other infor-
mational artefacts (i.e., information systems, databases, information flow
processes or documents). To exemplify, in [19], DOLCE is used to address
the polysemy of the term “inflammation”, which can be defined as a physi-
ological function, a characteristic portion of a body part, clinical condition
or as a diagnosis applicable to that condition.
– Ontology based data analysis: domain ontologies developed using a foun-
dational ontology were used to perform data integration and analysis. Here,
the usefulness of ontologies can be noticed in two ways. First, by ground-
ing the data on an ontology, researchers are able to identify errors, organize
the data and connect it with external sources. Second, by being efficiently
curated and by using reasoning, the ontology-grounded data can undergo
a more significant data analysis (e.g., using machine learning algorithms),
which can present useful results that support clinical decisions [14].
Answers to the question related to the claimed advantages and motivations
(RQ2 ) can be grouped in two main categories: improvement of ontologies and
improvement of data. Regarding the former category, foundational ontologies are
claimed to improve the semantic understanding of terms and avoid ambiguity
(as in the “inflammation” example above), enhance reasoning and prevent errors
(by the axioms added by foundational ontologies), speed up ontology develop-
ment (through the reuse of top-level categories and other ontologies grounded
on the same foundational one), improve interoperability (based on the idea that
ontologies that use the same foundational ontology tend to interoperate easier),
and facilitate ontology maintainability (by reusing categories from foundational
ontologies). Advantages related to data are: to enhance data consistency and
interoperability (by grounding data into a human-readable semantic artefact),
The use of Foundational Ontologies in Bioinformatics 7
and improve queriability (ontologized data can be queried using human-readable
terms).
A convergence to a small set of similar answers was observed when asking
RQ3. Several works mentioned the excessive complexity brought up by the use
of foundational ontologies, manifested in the time spent understanding class de-
scriptions and the high level of familiarity needed with background philosophical
theories. Additionally, the reduced usability in domain ontologies caused by the
added complexity from foundational ones is described as another disadvantage.
Unfortunately, just one paper specifically presented an empirical assessment
to test the claimed advantages and drawbacks (RQ4 ). Boeker et al. [3] conducted
a controlled trial to test the hypothesis that “students who received training on
top-level ontologies and design patterns perform better than those who only
received training in the basic principles of formal ontology engineering.” In the
assessment phase, students were asked to solve problems related to different
topics, producing a set of ontological models that were compared to a gold
standard. However, “the experiment showed no significant effect of the guideline-
based training on the performance of ontology developers”.
The last question aims to assess methodological rigour in the process of
building and evaluating ontologies (RQ5 ). A subset of 33 of the 51 selected pa-
pers developed a new domain ontology (using foundational ontologies). When
analysing this subset, we investigated how papers addressed ontology engineer-
ing [1] and evaluation [4]. The information about the methods employed by each
paper is available in the supplementary material.
Ontology Engineering. Five papers stated that ontologies were built fol-
lowing the OBO principles [22], which suggest the use of BFO as a foundational
ontology for ontology development. From the 33 papers that developed ontolo-
gies, only four explicitly mentioned the use of an ontology engineering method
proposed in the literature (i.e., Ontology Development 101 (OD101) [17] or On-
toSpec [10]).
Ontology Evaluation. Only two papers used Competency Questions [6] as
verification activities. On the other hand, sixteen works applied the developed
ontology to use case scenarios as means of validation, more specifically: in data
integration experiments, to support the development of an information system, in
data classification algorithms, in ontology mapping experiments, and in querying
and text mining tasks. Two papers performed instantiation [1] as a validation
step and four papers validated the ontologies with domain experts.
5 Discussion
The results of our review corroborates our observations, as we could identify
only one paper that ran an empirical experiment to test the claims for the use
of foundational ontologies. It is important to note that this does not mean that
these claimed (dis)advantages are under- or overrated. Actually, it indicates that
there is a clear need for evaluating these claims within the bioinformatics domain.
In other fields, there already exist several works that performed evaluations in
8 C. Bernabé et al.
an attempt to provide empirical evidence for the use of foundational ontologies,
and their results might be generalizable to bioinformatics. For instance, Keet [11]
conducted an experiment where participants, after receiving training in founda-
tional ontologies, had to choose between developing a “Computer Ontology”
from scratch or by reusing an OWL version of DOLCE or BFO. The study con-
cluded that advantages brought up by the use of foundational ontologies make
up for the time spent on getting acquainted with them. Verdonck et al. [24] ran
an experiment to test the differences between traditional conceptual modelling
and ontology-driven conceptual modelling, in which foundational ontologies were
used. The authors found that very few differences (e.g., number of ambiguities
and inconsistencies) are perceived when experimenting with simple and small
models. However, significant improvements are perceived when modelling bigger
or more sophisticated domains.
We also investigated how ontologies were built and evaluated. As mentioned
in the previous section, only 4 of 33 papers used a systematic ontology engineer-
ing method. Similarly, only 2 used a formal evaluation method (Competency
Questions) despite testing the ontology through application cases. Although the
use of foundational ontologies is claimed to impose a certain level of rigour dur-
ing ontology development and evaluation, these processes need to be supported
by additional techniques [1]. We argue that the use of ontology engineering and
evaluation methods should be an important concern in the research and de-
velopment of ontologies in bioinformatics, since these methods guide ontology
designers in defining aspects related to sustainability of ontologies (e.g., contin-
uous integration, manutenability, documentation), which consequently impacts
the long-term realization of the FAIR principles. In addition, ontology evaluation
intends to identify inconsistencies in the developed ontologies, thus improving
interoperability.
From the results overview, we observed that BFO’s popularity between the
set of selected papers can be related to the OBO principles proposed by the OBO
Foundry. These principles require the use of BFO as the standard foundational
ontology for the ontologies registered in the repository. We also noticed that the
boundaries between foundational and core ontologies are not clearly defined. For
instance, while some papers described Biotop as a core ontology for bioinformat-
ics (e.g., [20]), others classified it in the same level as BFO and DOLCE (e.g.,
[3]). In our work, Biotop was considered a core ontology.
Our findings also identified a need to define the best practices that can im-
prove ontologies for bioinformatics. Simon et al. [21] mentions that there are
understandable reasons for the ad hoc features of many biomedical ontologies.
Given the urgency to move from paper based to digital systems, ontologists were
forced “to make a series of uninformed decisions about complex ontological is-
sues”, which can be understood in the context of our work as the lack of formal
rigour in ontology development. The author also mentions that ontologists have
been tempted to seek immediate solutions to particular problems, but such be-
haviour cannot be accepted in a semantically interoperable world, since “ad hoc
solutions foster further ad hoc problems.”
The use of Foundational Ontologies in Bioinformatics 9
Finally, we recognize that some studies may not have been considered in our
analysis due to two reasons: (i) a paper may have used a foundational ontology
without explicitly mentioning it, or (ii) used an ontology that is in a grey area
between foundational and core ontologies. These can be seen as a trade-off in
using a method such as an SLM, since definitions (e.g., whether a foundational
ontology was used or not) should be clearly stated so the process can be system-
atically repeated. As a next step, we plan to manually include and discuss other
important studies (not captured by the SLM, but suggested by experts).
6 Conclusion
This paper described an SLM that was conducted to understand how founda-
tional ontologies are used in bioinformatics, as well as to identify the empirical
evidence in favour or against claimed advantages. Additionally, we investigated
the level of methodological adherence in papers that used foundational ontolo-
gies to construct domain ones. The understanding of how foundational ontologies
are used in bioinformatics can better drive future research towards the improve-
ment of ontology interoperability, and consequently the FAIRness of research
data. Our findings can imply two main conclusions. First, there is a lack of
empirical evidence for or against the use of foundational ontologies, and future
research should tackle this need. Second, this particular area of bioinformatics
should deal with ontology development and evaluation more formally and sys-
tematically. Consequently, we recommend that research in bio-ontologies should
study the creation or reuse of methods for ontology engineering (considering
phases from ontology requirements elicitation to testing and sustainability) and
ontology evaluation (encompassing both evaluation techniques and procedures
for application-based evaluation) supported by foundational ontologies. Thus,
as future work, we plan to investigate how foundational ontologies are used in
other fields and what can be reused to improve research in ontologies for bioin-
formatics.
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