=Paper=
{{Paper
|id=None
|storemode=property
|title=Representing Translations on the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-775/paper3.pdf
|volume=Vol-775
|dblpUrl=https://dblp.org/rec/conf/semweb/Montiel-PonsodaGCG11
}}
==Representing Translations on the Semantic Web==
https://ceur-ws.org/Vol-775/paper3.pdf
Representing Translations on the Semantic Web
Elena Montiel-Ponsoda, Jorge Gracia, Guadalupe Aguado-de-Cea, and
Asunción Gómez-Pérez
Ontology Engineering Group, Dpto. Inteligencia Artificial
Facultad de Informática, Universidad Politécnica de Madrid
Campus de Montegancedo s/n, 28660 Boadilla del Monte, Madrid, Spain
jgracia, emontiel, lupe, asun@fi.upm.es
http://www.oeg-upm.net/
Abstract. The increase of ontologies and data sets published in the
Web in languages other than English raises some issues related to the
representation of linguistic (multilingual) information in ontologies. Such
linguistic descriptions can contribute to the establishment of links be-
tween ontologies and data sets described in multiple natural languages
in the Linked Open Data cloud. For these reasons, several models have
been proposed recently to enable richer linguistic descriptions in ontolo-
gies. Among them, we find lemon, an RDF ontology-lexicon model that
defines specific modules for different types of linguistic descriptions. In
this contribution we propose a new module to represent translation re-
lations between lexicons in different natural languages associated to the
same ontology or belonging to different ontologies. This module can en-
able the representation of different types of translation relations, as well
as translation metadata such as provenance or the reliability score of
translations.
Keywords: multilingual Semantic Web, multilingual Linked Data, lemon
model, translation relations
1 Introduction
The Linked Open Data [1, 2] initiative has triggered the publication and linking
of data sets in the RDF [13] format, contributing in this way to semantically
structuring huge amounts of data on the Web. Thanks to the representation
format propounded by Linked Data, concepts are connected across resources,
breaking down the barriers imposed by data silos, and enabling machines to
smartly navigate the Web as a big data set. Currently, more than 250 data
sets containing more than 30 billion triples are available in the Linked Open
Data (LOD) cloud1 , ranging from domains as far apart as biomedicine, music
or geography. Governmental institutions, enterprises and the private sector have
realized the benefits and potential of such an initiative and have made their data
sets available for linking and exploitation by third parties.
1
http://www4.wiwiss.fu-berlin.de/lodcloud/state/
25
2 Lecture Notes in Computer Science: Authors’ Instructions
The launching phase of the LOD was led by English speaking countries, but
in recent years, the LOD cloud has also seen an increase in resources documented
in languages other than English. By having a quick look at the CKAN2 catalogue
of data sets, we come across the data.bnf.fr data set from the French National
Library, the GeoLinkedData.es data set of Spanish geographical data, Recht-
spraak.nl from the Netherlands Council of the Judiciary, or the FAO geopolitical
ontology with labels in English, French, Spanish, Arabic, Chinese, Russian and
Italian.
This proliferation of semantic data described in several natural languages
evidences the need for accounting for the linguistic information relative to on-
tologies and linked data because of several reasons. One of the main reasons is
that the linguistic descriptions of these resources help in finding and establishing
mappings between concepts and individuals of different ontologies and data sets
[22]. Another evident reason is that such descriptions contribute to a better ex-
ploitation of the data sets by tasks such as information extraction [19], natural
language generation [3], or multilingual data access [7], to mention but a few.
Several formats and annotation properties have been developed in the Se-
mantic Web to represent natural language descriptions associated to ontologies
and linked data, such as the rdfs:label [13] or skos:prefLabel [15] properties. Their
limitations have been discussed in several fora [5, 18, 14], and extensions or new
models have been proposed in the last years for the representation of linguis-
tic descriptions relative to ontologies and linked data in more principled ways.
Some of these models are SKOS-XL [16], LexInfo [5], LIR [18], or the recently
appeared lemon model [14]. Most of these models also provide some mecha-
nisms to allow for the representation of multilingual descriptions associated to
the same ontological representation. However, we argue that explicit relations
between descriptions in different languages, i.e., translation relations, as well
as translation descriptive metadata, would help in a more efficient exploitation
of these multilingual annotations. Moreover, they would also contribute to the
establishment of principled links between ontologies and data sets described in
multiple natural languages in the LOD cloud.
In this paper, we propose a representation mechanism of translations be-
tween labels in different languages associated to ontology terms. To that end, we
propose a metamodel in OWL which extends the lemon ontology, and which is
offered as a module of the lemon model. lemon is a linguistic model developed
in the framework of the Monnet3 project to represent lexical and terminological
descriptions relative to an ontology. The lemon extension we propose in this pa-
per enables the representation of translations in a separate linguistic layer, thus
leaving the original ontologies or data sources untouched. It also contributes to
the linking of ontologies and data sets described in different natural languages
in the Web of Data.
The rest of the paper is organized as follows. Section 2 summarizes the mech-
anisms that some Semantic Web formats or models have for linking linguistic
2
http://ckan.net/
3
http://www.monnet-project.eu/
26
Representing Translations on the Semantic Web 3
descriptions in several natural languages. In section 3, we analyze the problem of
translation relations in the context of the Semantic Web. After that, in section
4, we briefly present the lemon model. Thanks to the modular conception of
this model, we are now able to propose a translation module, i.e., a module to
explicitly represent translations in lemon. Section 5 will be devoted to a detailed
description of the translation module, and some examples will be provided to
illustrate the use of this module. Finally, we conclude the paper in section 6.
2 Related work
As it is well known, RDFS [13] and SKOS [15] rely on limited annotation prop-
erties to represent labels or linguistic descriptions associated to ontologies and
linked data. They also enable a simple form of multilingual labeling by using
language tags to restrict the scope of a label to a particular language (e.g.,
skos:prefLabel “bank”@en). This representation allows for indirect or non-explicit
links between or among multilingual labels, when associated to the same resource
in the data set.
Conscious of these limitations, SKOS developers worked on an extension of
SKOS called SKOS-XL [16] , that allows to make links explicit between labels as-
sociated to the same concept. This extension introduces a skosxl:Label class that
allows labels to be treated as first-order RDF resources, and a skosxl:labelRelation
property that provides links between the instances of skosxl:Label classes. In this
way, we can specialize the skosxl:labelRelation into a translation relation and ex-
plicitly link skosxl:Label instances in different natural languages.
The LIR [18] model also focuses on the representation of links between labels
within and across natural languages. This model was created with the purpose
of keeping the ontology and the linguistic information independent from each
other, so that lexical and terminological properties of labels could be further
described (e.g., part-of-speech, gender, terminological variants). The relations
provided by LIR to labels within the same natural language have lexical (has-
Synonym, hasAntonym) or terminological nature (hasVariant, hasAbbreviation,
hasTransliteration, etc.). And the ones between labels across different natural
languages have a translational nature (hasTranslation or hasScientificName).
Now, the relations provided by the SKOS-XL and LIR models, though being
useful for certain applications because of the explicitness of the hasTranslation
relation between labels in different natural languages, do not allow to account
for some aspects of the translation process that may also be relevant for cer-
tain applications. For instance, the difference between original and target label.
This may be interesting in the case that we have an ontology documented in
four natural languages, and we want to specify which labels (or which linguistic
descriptions) have been taken as the source in the translation process. Another
aspect to be considered could be the type of translation relation existing be-
tween labels (we will come back to this in section 3). Moreover, the provenance,
i.e., the resource from which translations have been obtained may also be the
kind of metadata that enriches the information about translation. Finally, it is
27
4 Lecture Notes in Computer Science: Authors’ Instructions
important to account for the adequacy and reliability of the translation in the
specific context of the ontology. An extension of these models would be required
to represent further translation metadata. However, we have chosen the lemon
model for this purpose, because its design principles make it specially appro-
priate for the Web of Data scenario. Firstly, lemon introduces a ‘well-defined
lexical-conceptual’ path between linguistic descriptions and ontology elements.
Secondly, lemon has been designed as a concise RDF model that captures com-
plex linguistic descriptions by dereferencing resources that contain them. And
thirdly, it is an extensible and modular model, which allows the use or inclu-
sion of certain modules if so required by the final application. These and other
features of the model will be further detailed in section 4.
Finally, we will refer to the LOD in Translation work 4 , in which a model
has been created to describe and retrieve translations in the LOD cloud relying
on resources that contain labels in different natural languages. This model takes
advantage of multilingual labels associated to resources by means of language
tags (as in rdfs:label “bank”@en, rdfs:label “Bank”@de, rdfs:label “banco”@es)
and retrieves available translations. Our purpose, on the other hand, is to con-
tribute to the creation of explicit translation links within the same data source
and across data sources, so that this and other systems can benefit from the
multilingual data in the LOD cloud.
3 Translation relations in the Semantic Web
Ontology localization [21, 8, 6] has been defined as the activity of adapting an on-
tology to the needs of a particular (linguistic and cultural) community. Method-
ological guidelines, tools and models have been developed to support the ontol-
ogy localization activity, which normally results in an ontology in which labels
are documented in multiple natural languages, what is the same, a multilingual
ontology [6]. Since the different linguistic versions are assumed to be pointing
to the same ontology concepts, it could be derived that they are all translations
of each other. However, if we have several terms in each language (synonyms or
term variants), we may want to unambiguously express which term variant in
language A is translation of which term variant in language B. At this point,
translation relations acquire significance.
Let us illustrate this with a simple example. In the FAO geopolitical ontol-
ogy mentioned in the introduction, one ontology term may describe the orga-
nization as such and have the labels “Food and Agriculture Organization” and
“FAO”. Translations of full form and acronym will be provided in the rest of
languages, and, ideally, explicit links will be created between the full forms and
the acronyms, respectively.
However, translation relations are not always so direct and simple. As claimed
in [8, 6], depending on the type of conceptualization represented in the ontology,
direct translations in the target language will be available or not. A distinction
4
http://sites.google.com/site/pierreyvesvandenbussche/apps/lod-in-translation
28
Representing Translations on the Semantic Web 5
is made between the so-called internationalized or standardized conceptualiza-
tions, and conceptualizations more prone to reproduce the vision of the world of
a certain community, the so-called, culturally-influenced domains. When local-
izing ontologies of these two types, translation relations may also need to be of
different types. To put it in other words, when dealing with internationalized do-
mains, i.e., technical or specialized domains of knowledge such as engineering or
medicine that have standards for processes and descriptions, and whose catego-
rizations usually reflect the common view of different cultures [17], we may find
translations for all terms describing the concepts in the ontology, since the same
conceptualization is shared among the languages represented in the ontology.
Contrary to that, when localizing ontologies representing culturally-influenced
domains, in which the granularity level of some concepts may differ from culture
to culture, we may come across mismatches that need to be solved to provide
adequate translations. Under this group we include domains such as law, geog-
raphy or the political and administrative organization of countries, universities,
and so on.
Imagine an ontology of financial institutions in Germany. One of the con-
cepts represented in the ontology may be Sparkasse (which we could generally
translate as savings bank in English). However, there may be differences between
these concepts concerning business purpose, ownership or governance of the in-
stitution. So, maybe, a more adequate translation of Sparkasse could be German
savings institution, although we usually tend to look for the closest equivalent
concept in the target language and get the term used to refer to it, i.e., savings
bank in this case. This simple example aims at illustrating the difference be-
tween ‘literal or documentary translations’, and ‘functional translations’5 . The
first type usually describes the concept in the target language, because there is
no exact equivalence in the target language. The second type looks for the clos-
est equivalence -though being conscious of the existence of disparities- because
it may be convenient for practical reasons. For instance, when aiming at inter-
operability (at a European or international level), near-equivalents are assumed
to match although a complete overlap between them does not exist.
According to this, we make a distinction between literal translations and
cultural equivalences. In the context of the Semantic Web, this distinction may
be quite simple to make. The literal translation would be pointing to the same
ontology concept, whereas the cultural equivalent would most probably belong
to an equivalent ontology documented in the target language. See figure 1 for an
illustration of this. Ontology A is an ontology of German credit institutions in
which labels have been translated into English, whereas Ontology B conceptual-
izes the structuring of British credit institutions in English. It would be highly
interesting to specify the links between these terms in a multilingual scenario.
For these reasons, we claim that further specifications of the translation relation
would contribute to envisage a true Multilingual Semantic Web.
5
Many practitioners and translation theorists agree on this difference and speak about
overt vs. covert translation [11], or documentary vs. instrumental or functional trans-
lation [20], respectively.
29
6 Lecture Notes in Computer Science: Authors’ Instructions
Ontology A Ontology B
(German) (English)
Concept A Concept B
Sparkasse German savings institution Savings bank
Fig. 1. Oversimplified example of literal translation and cultural equivalence links
4 lemon, an interchange model for the Multilingual
Semantic Web
The lemon model (lexicon model for ontologies) [14] is an RDF model of lin-
guistic descriptions that has been designed to a) be published with ontologies, b)
extend their lexical layer with as much linguistic information as needed, and c)
exchange the resulting lexical resources on the Web. Technical details and usage
of the model can be found at http://lexinfo.net/lemon-cookbook.pdf The main
features of the model can be summarized as follows:
– Linguistic descriptions are kept separated from the ontology, but their se-
mantics are defined by pointing to the corresponding semantic objects in the
ontology (what has been called ‘semantics by reference’ [4]).
– The model consists of a core set of classes (as described below) and several
modules capturing different types of lexical and terminological descriptions.
– Rich lexical and terminological descriptions are grouped into five modules:
linguistic properties (part-of-speech, gender, number...), lexical and termi-
nological variation, decompositions of phrase structures (representation of
multi-word expressions), syntactic frames and their mappings to the logical
predicates in the ontology, and morphological decomposition of lexical forms.
– Linguistic annotations (data categories or linguistic descriptors) are not cap-
tured in the model, but have to be specified for each lexicon by dereferencing
their URIs as defined in the repositories that contain them (for instance, the
ISOcat repository [12]).
The different types of linguistic descriptions captured by the model and its
main classes can be seen in figure 2. The core classes of the model are the ones
that form the main path between the Ontology and the lexical variants repre-
sented in the LexicalEntry class. The LexicalSense class provides a principled
link between an ontology concept and its lexical materialization (LexicalEntry).
30
Representing Translations on the Semantic Web 7
formVariant
LexicalForm
representation:String
Component
↳writtenRep:String
edge
Lexicon entry
decomposition† leaf
language:String canonicalForm
topic:Resource form otherForm Node
abstractForm
element† constituent:Resource
separator:String
lexicalVariant
LexicalEntry* phraseRoot
tree
Morph pattern (Word,Phrase,Part)
Pattern topic:Resource Frame leaf
synBehavior
nextTransform transform isSenseOf
sense
subsense marker synArg
Morph LexicalSense
Transform condition:Resource⁕
Argument
rule:string context:Resource
senseRelation semArg
definition:Resource⁑ optional:boolean
equivalent
example:Resource⁑ subjOfProp
onStem generates incompatible
objOfProp
narrower marker
reference isA
broader
extrinsicArg
isReferenceOf
Prototype prefRef
lexicalProperty Lexical
altRef
Ontology Any lemon
hiddenRef element
Category
* LexicalEntry has three subclasses: Word, Phrase, Part
⁑ definition and example are stated as nodes with a value
⁕ condition has subproperties propertyDomain and propertyRange
† decomposition and element may also be used with Frames and Arguments resp.
lemon
Fig. 2. Core classes and modules of the lemon model
Since ‘concepts’, as defined in ontologies, and ‘lexical entries’, as defined in lexi-
cons, cannot be said to overlap [10], the LexicalSense class provides the adequate
restrictions (usage, context, register, etc.) that make a certain lexical entry ap-
propriate for naming a certain concept in the specific context of the ontology
being lexicalized.
LexicalSense is also the class that is foreseen to provide the links between lex-
ical entries within and across languages. Four specializations of this relation are
provided: equivalent, incompatible, narrower and broader, as illustrated in fig-
ure 2. As the lemon model defines one lexicon per language, translation relations
could be inferred as lexical entries in different languages would be all pointing
to the same ontology reference. However, it is also foreseen to make this type of
relation explicit between lexical senses, in the case that, for instance, lexical en-
tries are not pointing to the same ontology reference, but belong to the linguistic
descriptions associated to other ontologies.
As such, the translation relation between lexical senses is a powerful mech-
anism to represent translations. Nevertheless, and as already pointed out in
section 1, when dealing with translations, additional properties of the transla-
tion relation need to be made explicit, such as reliability score, provenance, or
type of translation relation, as already introduced in section 2. In this sense,
the flexibility provided by the lemon model by means of modules allows us to
propose a so-called ‘translation module’, by reifying a translation relation be-
31
8 Lecture Notes in Computer Science: Authors’ Instructions
tween lexical senses into a class. The use of such a module could be exploited
by applications that require multilingual ontologies and want to keep track of
the relations between the lexical entries in different languages. This information
would be very valuable if translations have been automatically generated via an
ontology localization system (e.g., LabelTranslator NeOn Toolkit plug-in [9]).
5 lemon module for translations
In this section we describe the entities of the translation module in lemon 6 and
illustrate its use by means of some examples. Figure 3 shows the class diagram of
the translation ontology. Some classes are imported from the core of the lemon
ontology, namely Lexicon, LexicalEntry, Form, and LexicalSense.
Fig. 3. lemon Translation module
– Translation. This is the central class of the translation module. It mediates
the translation relation between lexical senses, and contains also informa-
tion that characterizes the translation process, such as a confidence level.
This confidence level will ultimately depend on the translation tools and
translation resources employed to obtain translations. We do not deal here
with the algorithms used for its computation, but it will typically combine
different features such as probabilities of translation systems, reliability of
translations resources, scores of disambiguation methods, etc.
6
It will be available at http://www.monnet-project.eu/lemon translation.owl
32
Representing Translations on the Semantic Web 9
– Literal Translation. It is a subtype of the translation class that corresponds
to the idea of literal translations mentioned above.
– Cultural Equivalence Translation. A subtype of the translation class that cov-
ers translations that are not literal, but close cultural equivalences between
the languages considered.
– Resource. It represents resources from which translations have been obtained.
– Lexical Sense. A sense links a lexical entry to the reference (ontology term)
used to represent its meaning.
– Lexical Entry. It is a container of the different forms and meanings of a
lexeme.
– Form. An inflectional form of an entry. It admits several representations
(written, phonetic, etc.).
– Lexicon. This class represents the whole lexicon. It has a language associated,
so it is assumed to be monolingual. Translations will typically connect entries
between different monolingual lexicons.
5.1 Examples of use of the lemon translation module
In order to illustrate the usage of the translation module, in this section we
provide some examples of the financial and politics domains.
LEXICONEN
LexicalEntry LexicalSense
“payment method” ONTOLOGY
http://purl.org/goodrelations/v1#PaymentMethod
LiteralTranslation
LexicalEntry LexicalSense
“medio de pago”
LEXICONES
Fig. 4. Example of literal translation
33
10 Lecture Notes in Computer Science: Authors’ Instructions
Figure 4 represents an ontology term extracted from the GoodRelations on-
tology7 . In lemon we would be able to associate as many lexicons in different
languages to the ontology as wished. In the figure, we show two lexicons that have
been associated to the ontology: one lexicon with English descriptions and the
other with Spanish descriptions. Both lexicalize in different languages the same
ontology concept, namely, http://purl.org/goodrelations/v1#PaymentMethod. Each
lexicon contains a lexical entry and a lexical sense representing the ontology con-
cept in each language. The lexical sense belonging to the English lexicon would
be the sourceLexicalSense, and the one of the Spanish lexicon would be the tar-
getLexicalSense, since the ontology was conceived in English. The provenance of
the translation would be specified at the Resource class. It could be an on-line
resource (machine translation service), a lexicon or terminology of the domain,
or even a human translator. A confidence value could also be assigned to the
translation by means of the confidenceLevel property of the Translation class. Fi-
nally, we would relate these two translations by means of the LiteralTranslation,
subclass of the Translation class. This would mean that in the specific con-
text of the ontology being lexicalized and localized, the target lexical
sense provides a description or literal translation of the term, which
is to be used in the context of the original ontology. It is highly proba-
ble that the Spanish translation “medio de pago” is also its cultural equivalent,
which would mean that the same concept exists in the Spanish financial system
and has been termed as the literal translation. So in this case, both translation
relations would be valid.
LEXICONEN
ONTOLOGY
LexicalEntry LexicalSense http://www.gobernontology.co.uk/prime
“Prime Minister”
Ontology term
Cultural Equivalent Translation
Ontology term
LexicalEntry LexicalSense
http://www.ontologiagobierno.es/presidente
“Presidente del Gobierno”
LEXICONES
ONTOLOGY
Fig. 5. Example of Cultural Equivalence
1
7
http://www.heppnetz.de/ontologies/goodrelations/v1
34
Representing Translations on the Semantic Web 11
Now, let us have a look at figure 5. This aims at illustrating cultural equiva-
lents between political systems. Here we have two ontologies, each one represent-
ing a different political system, and each one documented in a different natural
language. The concept of “Prime Minister” in the British political system and
the concept of “Presidente del Gobierno” in the Spanish political system are not
exact equivalents, but can be considered the closest equivalents in the respective
cultures. This is why we would use the class CulturalEquivalenceTranslation to
relate the two lexical senses that we assume would belong to two lexicons associ-
ated to two different ontologies. Such a relation would indicate that these
two terms are substitutable or translations of each other, when look-
ing for interoperability and referring to (close) equivalents in different
languages and cultures, whose extention may not completely overlap.
In this case, we could also include literal translations of each lexical entry in
the respective lexicons. In the English lexicon we could include the Spanish lex-
ical entry “Primer Ministro Británico”, which would be a literal translation in
Spanish. In the same way, we could also add the lexical entry “Spanish Presi-
dent” or “Spanish President of the Government” in the Spanish lexicon. These
translations would be related to each other by the LiteralTranslation class.
6 Conclusions
The publication of ontologies and data sets in multiple natural languages has
raised some issues related to the representation of the linguistic descriptions rel-
ative to ontologies. In the context of Linked Data, this takes on more importance
since ontologies and data sets described in different natural languages have to be
linked to each other. Moreover, such natural language descriptions have proven
essential in enabling the exploitation of semantically structured knowledge by
language-based tasks. With the purpose of establishing explicit links between the
linguistic descriptions associated to ontologies and linked data in several natural
languages, in this paper we propose an extension of the lemon model to represent
translation relations. This translation module allows us to differentiate between
literal and cultural equivalence translations. In addition to that, we can provide
metadata relevant to the localization process that may be of great interest when
relying on the automatic translation of ontologies.
As future work we plan to carry out some experiments to provide statistics
on the impact of such translation relations in the Multilingual Semantic Web,
specifically the distinction between literal translations and cultural equivalences.
We also aim at investigating the implementation of algorithms that would au-
tomatize this process.
Acknowledgments. This work is supported by the EU project Monnet (FP7-
248458), and by the Spanish national project BabeLData (TIN2010-17550).
35
12 Lecture Notes in Computer Science: Authors’ Instructions
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