=Paper=
{{Paper
|id=Vol-3127/paper-11
|storemode=property
|title=A Survey of Multilingual OWL Ontologies in BioPortal
|pdfUrl=https://ceur-ws.org/Vol-3127/paper-11.pdf
|volume=Vol-3127
|dblpUrl=https://dblp.org/rec/conf/swat4ls/Gillis-WebberK22
}}
==A Survey of Multilingual OWL Ontologies in BioPortal==
https://ceur-ws.org/Vol-3127/paper-11.pdf
A Survey of Multilingual OWL Ontologies in
BioPortal
Frances Gillis-Webber and C. Maria Keet
Department of Computer Science, University of Cape Town, Cape Town, South Africa
{fgilliswebber@cs.uct.ac.za,mkeet@cs.uct.ac.za}
Abstract. The internationalisation goal for OWL sought to offer
support for multilingual ontologies. User-displayable labels were
suggested as a way to realise this, by means of rdfs:label. However,
because each label is a language-tagged string, this hampers accurate
representation of strings in languages that require grammatical features
such as inflected forms and gender. At least eight linguistic models have
been proposed to address this key shortcoming, with OntoLex-Lemon
now the de facto standard. The purpose of this survey was to determine
if there has been any adoption of linguistic models within OWL
ontologies. As OWL ontologies are widely used in the biomedical
domain, the survey was limited to those ontologies in NCBO BioPortal,
a biomedical repository. The results indicate that OntoLex-Lemon was
not used in any production OWL ontology at time of review, nor that
of any other linguistic model. In addition, the adoption rate of
multilingualism in OWL ontologies in BioPortal was observed to be 5%,
with English the primary language, followed by French and German.
Keywords: OWL ontologies · Linguistic Model · Multilingualism.
1 Introduction
There has been wide adoption of ontologies in the biomedical domain [14, 15,
40]. Multilingual ontologies are highly relevant, particularly as their use in a
medical context affects a diverse range of language speakers. In the context of a
global pandemic, there are requirements for biomedical and healthcare tools, as
well as data integration at the national level. When considering the many
languages spoken in the world, this suggests multilingual bio-ontologies will be
of use. An example is OpenMRS, the medical record system used in 40
countries [31], which in turn uses SNOMED CT, a vocabulary of medical terms
formalised in UMLS and OWL1 [29]. SNOMED CT’s multilingual range
extends to French, Danish, Dutch, European Spanish and Swedish only [34].
The lack of broader multilinguality is recognised by SNOMED CT as a
possible barrier to adoption [34]. The translation of SNOMED CT into other
1
SNOMED CT is only available on BioPortal in UMLS format, with English labels,
so it was not able to be included in the review.
Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 Gillis-Webber and Keet
languages is not without challenge. For instance, Keet and Khumalo took steps
to translate SNOMED CT into isiZulu (a Niger-Congo B (‘Bantu’) language),
with the aim to provide localised electronic health records and patient
discharge note generation [17]. However, the translation of OWL ran into
encoding issues due to the ubiquitous ‘part of’ relation as there is no single
term to denote this relation in isiZulu. Instead, it is constructed depending on
the category of things that play the part and the whole [16].
When developing multilingual ontologies, there are several options [2, 13, 26].
The standard approach is to set multiple language-tagged annotations using
rdfs:label [11, 39]. The use of a linguistic model, such as OntoLex-Lemon [9,
23], is another possible approach, where each language-specific sense (from lexical
entries) is associated with an ontological entity. Mapping models between classes
or individuals from monolingual ontologies is also an option [38], where SKOS
is typically used to align individuals [24]. Language-tagged annotations is the
simplest way to model multilinguality, however its limitations are numerous,
particularly when representing languages that have grammatical features such
as inflected forms, gender, dependent prepositions, double negation, concordial
agreement, and agglutination (see [16–18]). An example of grammatical gender
is given by the “linked to” object property in the Organization Ontology2 , which
has two labels in Spanish: “está relacionado con” and “está relacionada con”.
Use of either one is determined by the gender of the noun of the class in the
domain position, or the subject of the sentence. As rdfs:labels are relied upon
in question-answering and similar systems [10], it is more difficult to represent
languages that have one or more of the above features in a way that also allows
for accurate verbalisation of ontologies.
OntoLex-Lemon and its precursor, Lemon [20, 22], has been proposed as a
possible solution by providing rich linguistic grounding for ontologies. Other
linguistic models, such as LingInfo [6], LexOnto [8], Linguistic Information
Repository (LIR) [25, 33], Linguistic Watermark 3.0 [32], LexInfo [5, 7], and
GOLD [12] have been proposed to solve similar problems. OntoLex-Lemon has
shown significant adoption within the Linguistic Linked Data cloud [30], with
it now being the de facto model for linguistic representation [9]. As a step in
the analysis of uptake of multilingual ontologies, we were interested to see if
the adoption of a linguistic model such as OntoLex-Lemon has extended to
OWL ontologies as well. We did this by collecting and analysing ‘production’
ontologies from BioPortal. Of the multilingual ontologies identified, we also
wanted to identify the use of more expressive OWL profiles in these ontologies,
where this expressivity allows for more complex reasoning, useful for
question-answering and other natural language generation.
The primary purpose of this study was thus to identify the ratio of
multilingual OWL ontologies to monolingual ones, the modelling options used
for multilingual ontologies, as well as the OWL profile used. A core aspect of
multilingualism for ontologies is the identification of elements using URIs. An
identifier is a fragment of the URI that uniquely identifies the entity in
2
https://lov.linkeddata.es/dataset/lov/vocabs/org
A Survey of Multilingual OWL Ontologies in BioPortal 3
question. A URI fragment can be opaque (semantic-free) or descriptive
(meaningful) [3, 19, 27]. For a descriptive identifier, there is a direct
relationship between the natural language term used as an identifier and its
semantics [21, 37]. If an opaque identifier is used as part of the URI, then a
human agent will require an additional sign in order to interpret the URI, and
this is realised using the rdfs:label annotation. OBO is well-known to have
taken a natural language-independent approach [35], with opaque URI
identifiers and mandatory labels, which also assume that if there is a
translation, it would be a 1:1 translation of the term. In addition, due to
OBO’s different take with identifiers, we wanted to determine if this approach
has extended to any other ontologies.
We also analysed coverage of each natural language compared to the total
entity count within an ontology. As part of this work, we used the LLC lang metric
for multilinguality identified by Ell et al. [11], that measures the completeness
for a given language within a corpus. The results show that there are only a few
production ontologies in BioPortal that are multilingual to a greater or lesser
degree. Of those multilingual ontologies, all use the multilingual labels option.
Three of the ontologies had less than 5% language-specific coverage.
In the next section, the methodology for data collection and preparation is
described. In Section 3, the ontologies are analysed for their multilingual aspects
and discussed. We close with the conclusion in Section 4.
2 Methodology and Overview
Data collection was conducted using the REST API3 provided by BioPortal.
The steps to collect the required data are listed below.
1. Using the /ontologies endpoint within Postman4 , a list of ontologies were
downloaded and saved as a JSON file.
2. Using the /categories endpoint, the process in Step 1 was repeated to get
the list of categories, with both files then imported into a MySQL database.
3. A script was written which queried each ontology using the
/ontologies/{acronym}/latest submission and
/ontologies/{acronym}/categories endpoints. The following data was
then saved for each ontology: the list of linked categories, the ontology
language used, the namespace acronym, URI, status, and description.
4. Only ‘OWL’ ontologies were selected, with all other formats excluded.
5. Only ‘production’ ontologies were selected, with all other statuses excluded.
6. Ontologies in the ‘Upper Level Ontology’ and ‘Vocabularies’ categories were
then excluded.
7. Thereafter, each remaining ontology was downloaded, using the
/ontologies/{acronym}/download endpoint.
3
http://data.bioontology.org/documentation
4
https://www.postman.com/
4 Gillis-Webber and Keet
Fig. 1. Labels: an example of multilingual annotations in a language-independent
ontology using a class from OBI.
BioPortal provides categories into which each ontology is classified. Ontologies
in the ‘Upper Level Ontology’ were excluded in Step 6 as the focus was on
domain ontologies. Ontologies in the ‘Vocabularies’ category were also excluded
as these are controlled vocabularies and thesauri, which are typically lightweight
ontologies at best. For Step 7, if the file size was large and resulted in timeout
errors, the file was manually downloaded. If the Download endpoint returned
an error for an ontology, the ontology would be manually inspected online. If
the latest submitted version had an ‘Error RDF’ annotation, then no further
download attempt was made.
Steps 1–7 were run on the 30/12/2020. Step 1 resulted in a dataset of 912
ontologies5 . At the end of Step 7, a dataset of 220 ontologies remained. The
review of ontologies for their multilingualism were conducted on this dataset. The
possible options were multilingual labels, linguistic models, and mapping models.
A diagrammatic representation of the multilingual labels option is shown in
Figure 1 for the class ‘Enzyme’ from the Ontology for Biomedical Investigations
(OBI) [4]. Alignment of monolingual instances using SKOS is shown in Figure
2. An example linguistic modelling option is shown in Figure 3 using OntoLex-
Lemon for the class from Figure 1.
Each ontology file was loaded in memory, using a script to loop through
each line therein. For multilingual labels, @ and xml:lang was searched for,
and if found, that line was saved to the database. For linguistic models, any
line which contained the namespaces: ontolex (OntoLex-Lemon), linginfo,
lexonto, lexinfo, gold and lemon was saved to the database. Likewise for
mapping models, any line which contained skos was saved to the database.
Thereafter, these saved rows were manually inspected for their multilingual
aspects. The natural languages used in each ontology were easily identified;
however, identification of those as primary language(s) was more subjective. To
determine an ontology’s primary language, an ontology was manually
inspected, focussing on the labels used, and the consistency thereof for each of
its languages. To qualify as language-independent, there would have to be
near-equal support for the natural languages therein, as well as opaque URI
identifiers. We applied LLC lang per multilingual ontology to support
identification of those as primary language ontologies.
Of the multilingual ontologies identified, the OWL profile(s) of each were then
determined using the OWL Classifier [1]. Our inspection was limited to OWL 2
only. For the ontologies classified as OWL 2 Full, the profile violations for OWL
5
The dataset is available here: https://fynbosch.com/article-2021-bioportal-review
A Survey of Multilingual OWL Ontologies in BioPortal 5
Fig. 2. Mapping model: an example using SKOS to align monolingual instances.
Fig. 3. Linguistic model: an example where two lexical entries from different language
lexicons are associated to an OBI entity, if it were to use OntoLex-Lemon.
2 DL were examined and if determined to be related to annotations only, the
ontology was reclassified as OWL 2 DL as these violations can be patched easily.
3 Assessment of Ontologies for their Multilingualism
The dataset of 220 ontologies was analysed on the different types of modelling
options. Only 11 were identified as being multilingual, which are listed in Table
1. As can be observed, they all use the multilingual labels option and mostly
opaque identifiers. The latter is likely a follow-on effect from OBO, which has a
principle that identifiers should not have a label meaningful to humans [35, 36].
Of those ontologies identified with more than one primary language, this
suggests they are language-independent. Only four ontologies were identified as
being language-independent: ATOL, CIDOC-CRM, PDRO, and RADLEX.
The remaining ontologies were identified as being of a primary language, with
translations into other languages. Only one primary-language multilingual
ontology was identified to be in a language other than English.
6 Gillis-Webber and Keet
Table 1. Classification of Identified Multilingual Ontologies in BioPortal
Primary Other Modelling
Ontology Type Identifiers
Language(s) Language(s) Option
OWL 2 Multilingual
DL†
Animal Trait Ontology for en, fr - Opaque
Labels
Livestock (ATOL)
OWL 2 de, el, en, fr, pt, Multilingual
CIDOC Conceptual Reference - Opaque
Full ru, zh Labels
Model (CIDOC-CRM)
OWL 2 Multilingual
WHO COVID-19 Rapid en pt-br Opaque
Full Labels
Version CRF Semantic Data
Model (COVIDCRFRAPID)
Multilingual
Clinical LABoratory Ontology N/A‡ en fr Opaque
Labels
(LABO)
OWL 2 Multilingual
Ontology of Chinese Medicine en zh Opaque
DL Labels
for Rheumatism (OCMR)
Multilingual
Ontology of Units of Measure N/A‡ en ja Descriptive
Labels
(OM)
OWL 2 Multilingual
Emergency Care Ontology fr en Descriptive
DL Labels
(ONTOLURGENCES)
Multilingual
The Prescription of Drugs N/A‡ en, fr - Opaque
Labels
Ontology (PDRO)
OWL 2 Multilingual
Radiology Lexicon (RADLEX) de, en - Opaque
DL Labels
OWL 2 Multilingual
DL†
Uber Anatomy Ontology en de, es, fr Opaque
Labels
(UBERON)
OWL 2 Multilingual
DL†
Viral Disease Ontology Trunk en de Opaque
Labels
(VDOT)
† Classified as OWL 2 Full, but re-classified to OWL 2 DL due to minor profile violations relating to annotations.
‡ One or more of the imported ontologies could not be loaded in the OWL Classifier, so the profile is undetermined.
We highlight the multilingual aspects of some of the identified ontologies that
prompted classification as ‘language-independent’ or ‘primary language’; Table 2
refers. Both CIDOC-CRM and RADLEX are language-independent ontologies.
Although CIDOC-CRM does indeed have its rdfs:comments only in English,
comments are ignored in parsing, whereas annotations and labels remain “first-
class citizens” of the ontology, and its rdfs:labels were observed to provide
near-complete coverage in English, French, German, Greek, Portuguese, Russian
and Chinese, with opaque identifiers as well; hence, the classification as being
a language-independent ontology. In RADLEX, English and German are the
primary languages, with opaque identifiers. The ontology has its own annotation
properties: Preferred_name and Preferred_name_German, with both observed
to be used in equal proportion. The ontology also has definitions in English only,
but as these are imported from Medical Subject Headings (MeSH), the ontology
has been classified as a language-independent ontology.
COVIDCRFRAPID, ONTOLURGENCES and UBERON are three primary
language ontologies. In COVIDCRFRAPID, in addition to English rdfs:labels,
the occasional use of Brazilian Portuguese was also observed. The ontology also
made use of skos:prefLabel, but only English was indicated. An OWL fragment
showing multilingual annotations for COVIDCRFRAPID is shown in Listing 1.
1 Prefix (:= < http :// purl . org / vodan / w h o c o v i d 1 9 c r f s e m d a t a m o d e l / >)
2
3 A n n o t a t i o n A s s e r t i o n ( rdfs : label : Chest_pain " Chest pain " @ en )
4 A n n o t a t i o n A s s e r t i o n ( rdfs : label : Chest_pain " Dor no peito " @ pt - br )
5 A n n o t a t i o n A s s e r t i o n ( skos : prefLabel : Chest_pain " Chest pain " @ en )
Listing 1. Example multilingual annotations from COVIDCRFRAPID
A Survey of Multilingual OWL Ontologies in BioPortal 7
In ONTOLURGENCES, French is the primary language, with descriptive
identifiers, also in French. The ontology made use of SKOS’ prefLabel,
hiddenLabel and definition properties, with a limited number of English
prefLabels observed. In UBERON, English is the primary language, with
German, Spanish and French used as well. The ontology has used the
OboInOwl metamodel to define SKOS-like relations for classes, such as:
oboInOwl:hasExactSynonym and oboInOwl:hasRelatedSynonym. It was
observed that some exact/related synonyms are provided in other languages,
but not all equally. Very few of the English annotations were language-tagged.
We expand on the metrics for multilingual ontologies identified, shown in
Table 2. The total number of classes, properties and named individuals for an
ontology, prior to any imports, is shown in the ‘Elements’ column. LLC lang is
then applied to each applicable natural language, where its coverage is indicated
as a percentage relative to the total number of elements for that ontology. Using
UBERON as an example, coverage is so low for German, Spanish, and French,
that it hardly qualifies as a multilingual ontology.
Table 2. Label Metrics: Language-specific Coverage Compared to Total Elements for
each Ontology.
Ontology Elements de el en es fr ja pt pt-br ru zh
ATOL 2 352 - - 100% - 100% - - - - -
CIDOC-C. 372 92.5% 87.4% 99.7% - 87.4% - 87.4% 87.1% 90.9%
COVIDC. 492 - - 79.9% - - - - 56.7% - -
LABO 116 - - 67.2% - 13.8% - - - - -
OCMR 3 481 - - 5.8% - - - - - - 1.3%
OM 836 - - 35.0% - - 3.0% - - - -
ONTOLU. 10 092 - - 28.2% - 99.0% - - - - -
PDRO 239 - - 74.1% - 62.3% - - - - -
RADLEX 45 852 - - 99.7% - 100% - - - - -
UBERON 16 171 0.01% - 94.0% 0.02% 0.2% - - - - -
VDOT 116 66.4% - 100% - - - - - - -
Size of the ontology varied significantly per multilingual ontology. For
example, CIDOC-CRM required only 372 annotations, but RADLEX required
45 852. RADLEX required significantly more effort to achieve complete
coverage to that of CIDOC-CRM. For some smaller ontologies, such as LABO
and PDRO, where there was incomplete coverage per natural language, the use
of multilingualism was limited at best. Seven of the eleven multilingual
ontologies were identified to have relations with OboInOwl [28] or other OBO
relations. The current modelling practice for multilingual ontologies is
multilingual labels, with opaque identifiers. Of the languages identified, English
was predictably represented in every multilingual ontology, followed by French
(6) and German (4).
Only ‘production’ ontologies were considered, but if ontologies with
alternative statuses were also analysed, it is possible that the newer ontologies
put into production in the past months may have used an alternative modelling
option. The evidence indicates that there has been no uptake of any linguistic
8 Gillis-Webber and Keet
model for OWL ontologies. Likewise for mapping models, there was no use of
SKOS for multilingual alignments. Due to insufficient data, we were unable to
determine why this is the case from the analysis conducted. A possible reason
may be insufficient tooling—there are limited tools to support the
development, maintenance, and coordination of multilingual ontologies or
multiple monolingual ontologies. It is unclear if the low number of multilingual
ontologies is a bias in BioPortal or the resources themselves. It might be the
case that there is limited need for multilingualism depending on the subject
domain, such as a higher relevance in healthcare and administration rather
than for biomedical research. We are currently finalising a more comprehensive
review of multilingualism in ontologies, including ontologies from other subject
domains. Its outcomes will be used in other ontologies and tools, such as the
MoReNL project6 , whose foundations assist in the development of
ontology-driven multilingual tools.
4 Conclusion
The assessment of use of multilingualism in production-level BioPortal
ontologies revealed a low adoption rate. The 11 out of the 220 that were
multilingual, predominantly used opaque identifiers with multilingual labels.
The ratio of multilingual OWL ontologies to monolingual ones was thus
identified to be 5%, however coverage of multilingual labels did not exceed 50%
for 3 of these ontologies. There was also no adoption of any linguistic model.
The OWL profile was identified to be primarily OWL 2 DL, but some
ontologies would require minor patching of the annotations to achieve this. Of
interest here is that these annotation profile violations were not identified when
developing the ontology, so a better mechanism may be needed to address this.
For the modelling options, both multilingual labels and linguistic models
present areas of opportunity for future research. Of the ontologies analysed, the
reuse of OBO-related ontologies was observed to be substantive, and this presents
the opportunity to represent the shared lexicons for each using a linguistic model.
Acknowledgements This work was financially supported by Hasso Plattner
Institute for Digital Engineering through the HPI Research School at UCT
[FGW] and the National Research Foundation (NRF) of South Africa (Grant
Number 120852) [CMK].
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