=Paper=
{{Paper
|id=Vol-3636/paper9
|storemode=property
|title=Pattern Comparison of is-a Concepts for Ontology Localisation
|pdfUrl=https://ceur-ws.org/Vol-3636/paper9.pdf
|volume=Vol-3636
|authors=Frances Gillis-Webber
|dblpUrl=https://dblp.org/rec/conf/wop/Gillis-Webber23
}}
==Pattern Comparison of is-a Concepts for Ontology Localisation==
https://ceur-ws.org/Vol-3636/paper9.pdf
Pattern Comparison of is-a Concepts for Ontology
Localisation
Frances Gillis-Webber1
1
Computer Science Department, University of Cape Town, Cape Town, South Africa
Abstract
When a domain is represented in an OWL ontology using a natural language πΏ1 , the perspective or
viewpoint of that language is the lens through which the domain is interpreted. If this same ontology is
made multilingual, annotations are typically added to each ontology element for each additional language
πΏπ , where it is assumed there is a 1-1 mapping. As part of a larger project to localise an ontology where
ontology concepts are refactored to a target viewpoint, the potential mismatches needed to be identified.
In this paper, the pattern matches and mismatches when localising is-a concepts is detailed, with a focus
on the axiomatisation of a concept, as well as any annotations thereof. Each source and target concept
is abstracted to a pattern (consisting of the main axiom pattern and the superclass as a sub-pattern).
Nine patterns have been identified for the axiomatisation of an is-a concept, and another five patterns
identified for the content of the annotation, when annotating an element using the labels approach.
Keywords
OWL ontologies, patterns, ontology localisation
1. Introduction
Within an OWL ontology, there is a commitment to a conceptualisation by its logical language,
with natural language used from, for example πΏ1 , for each concept name, property name, and
other axioms. To make the ontology multilingual, a natural language-specific annotation is
typically added to each concept name and property name for each additional natural language,
where it is assumed there is a 1-1 mapping. The result is the underlying axioms (the semantic
layer of the ontology), according to πΏ1 , remain unchanged. Ontology localisation differs to
ontology multilingualisation in that it is a re-engineering activity where an ontology π is adapted
so as to provide a localised view, where a localisation can pertain to a natural language, culture, or
geopolitical environment [1]. For the word βviewβ, the definition of a viewpoint is used, namely
the βmental position or attitude from which a subject or question is consideredβ [2]. Ontology
localisation, like that of ontology multilingualisation, is typically done in the annotation βlayerβ,
as adaptation of the semantic layer is a non-trivial task [1].
As part of a larger project for ontology localisation, where both the semantic and annotation
layers of the ontology are adapted using an (automated) model-driven engineering approach,
the types of mismatches for a concept between a source ontology and its target adaptation were
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first identified [3]. Of the axiom mismatches, the pattern (mis)matches then had to be identified,
distinguishing between is-a and part-of relations. In this paper, the focus is on the is-a patterns
of concepts when transforming an ontology to a language-specific viewpoint. By identifying
the different patterns, refactoring rules can then be determined for each pattern transformation.
When considering the transformation of a concept from a source to a target viewpoint, a
concept can be broadly classified by the lexicalisations of the source and target languages
thereof:
1. A concept which has a lexical realisation (that is, a word or smaller unit such as a
morpheme) for the natural languages used for both the source and target viewpoint.
2. A concept which has a lexical realisation in the source language; in the target language,
there is no lexical realisation however the concept is known.
3. Similar to (2), except that the concept is not known in the target language.
4. For both the source and target language, the concept is known, however neither have a
lexical realisation. The concept is known in a third language.
When there is no lexicalisation of a concept in a (target) language, this is known as a lexical gap.
Lexical gaps can be further refined as linguistic and referential gaps, where the former refers to
a concept which is known in the target language (albeit unlexicalised), and the latter, where it is
not known at all [4]. Referential gaps tend to apply to concepts which are more culture-bound.
Four examples of ontology localisation differences for three target languages have been
identified, presented in Table 1. For each example, the viewpoint is the natural language
given for the source and target. UC1 is an example of direct equivalence, where South African
English speakers use the term βrobotβ when talking of a traffic light. The class from SNOMED
CT serves as an example of the source concept in an ontology1 . UC2 is another example
of equivalence like that of UC1, however, as the superclass is not the same, this is indirect
equivalence. UC3 is an example of granularity mismatch, where the two French terms are
equivalent to the single English term. This is an oft-used example in the literature on the
mismatch between a source target language, cf. [1, 5]. The class from the SWEET ontology
serves as an example of the source concept2 . UC4 is another example of granularity mismatch,
except that there is differing granularity on both sides. Each share the same superclass. Due to
space constraints, the axiomatisation of the source and target concepts is not given here. Please
see https://fynbosch.com/article-2023-wop for the axiomatisations of each.
A concept can be thought of as a space with fuzzy boundaries, where a source viewpoint
may divide the same concept space differently to that of the target viewpoint. To identify the
differences between a source and target concept space, the axioms of each are abstracted to a
pattern, consisting of a main pattern and a sub-pattern, where four patterns have been identified
for the main pattern, and five for the sub-pattern. When considering lexicalisations as well, a
further five patterns have been identified pertaining to annotations.
1
http://purl.bioontology.org/ontology/SNOMEDCT/257720004
2
http://sweetontology.net/realmHydroBody/River
Table 1
Examples for a source and target viewpoint. The natural language used for each is indicated by the
language code in brackets.
Source Target
UC1 traffic light (en) robot (en-sa) In South African English, a different term is used to that of
British or USA English.
UC2 spoon (en) lepel (af) The meaning is the same, except that neither share the same
hypernym when given in a dictionary. A βspoonβ is a utensil,
while a βlepelβ is glossed as a βtoolβ.
UC3 river (en) rivière, fleuve (fr) Granularity mismatch where the concept space of French is more
specific to that of English.
UC4 city, town, village, ville, village, Granularity mismatch where French is more specific.
hamlet (en) bourg, bourgade,
hameau (fr)
The remainder of the paper is structured as follows. In Section 2, related works are briefly
discussed. In Section 3, a concept space, within the context of an OWL ontology, is defined.
This follows with the patterns, given in Section 4. There is a discussion in Section 5 as well as
an abstraction of each of the use cases. The paper concludes with Section 6.
2. Related Work
Focussing first on ontology localisation, in a paper by Cimiano et al. [1], three use cases were
presented, of which only two are detailed here due. For the first use case, an ontology element
is associated with three terms for Spanish, English, and Catalan respectively. The goal was
a common conceptualisation between the three languages, with the result that there is a 1-1
mapping between the conceptualisation and the terms. For the second use case, Princeton
WordNet [6, 7] and other language WordNets were given as an example, although WordNet is a
semantic network only, and not an ontology. All the other language WordNets use the English
WordNet as a pivot. In both of these use cases, adaptation of the ontology/resource is done in
the annotation layer only, with the semantic layer remaining unchanged.
In a paper by Montiel-Ponsoda et al. [8], the authors proposed an approach to associate
linguistic information with ontology elements, using a model called Linguistic Information
Repository (LIR). Here, lexical entries are created for each viewpoint (cultural and natural
language-specific), each associated with the same ontology element, and then relations are
established between each of the lexical entries where appropriate. This approach affects the
annotation layer only. UC3 is included as an example, however, in the case for French, there
are two lexical entries for rivière and fleuve, whereas the goal of this paper is to have two
classes. Brief mention is made of adapting an ontology in the semantic layer, but this is not
expanded upon. Similar to LIR, OntoLex-Lemon is a model intended to represent linguistic
information, where meaning of a lexical entry is given by an ontological reference [5]. UC3 is
also given as an example, where the lexical entry for the term river and the lexical entries for
rivière and fleuve are determined to be equivalent as they are each associated with the same
ontological element, such as :River in DBPedia. However, as criticised by Hirst [9], ontological
references are simply not granular enough to account for the meaning differences between
natural languages. OntoLex-Lemon also has a vartrans module which provides for translation
between source and target lexical senses. Of the examples given, a 1-1 mapping is assumed,
with no mention made of lexical gaps. The categorisation of each of the translation types is
given by Translation Category Reference RDF Schema (TRCAT) [10], with this categorisation
determined to be insufficient in [11]. OntoLex-Lemon applies to the annotation layer only.
If ontology alignment is considered, then alignment can be done between source and target
ontology elements, at class-level or at label-level [12]. However, in the examples given, it
is assumed that alignment is always between two heterogenous sources, whereas this paper
assumes the source ontology and the transformation thereof to be homogenous (that is, same
modelling style, etcetera). If ontology design patterns (ODPs) are considered from Ontolo-
gyDesignPatterns.org, there are several categories of ODPs that are relevant: re-engineering,
alignment, and lexico-syntactic [13]. For re-engineering, of the ODPs given, only heterogenous
sources are considered, for example, from a classification scheme to an ontology. For alignment,
heterogenous sources are again the only consideration. For lexico-syntactic, an equivalence
pattern is given between a source and target label, but no mention is made of lexical gaps
(fleuve from UC3 in English) or terms with overlapping meaning (UC4). Lastly, in a paper by
Fillotrani and Keet [14], correspondence patterns of mappings between TBoxes are considered,
however again, the sources are heterogenous, and localisation is not considered. Of each of the
localisation approaches reviewed, localisation is typically done in the annotation layer, with the
underlying axioms remaining unchanged.
3. Definition of a Concept Space
Using the definition of a concept space from [3], it is extended so as to include annotations. A
concept space can be defined as a 7-tuple CS = < πΆπ, VP, πΏπΌ , ππΆ, π΄π, πΌ ππ, π΄ππ >, where πΆπ is the
concept, represented as a natural language description, VP is the viewpoint expressed as a URI,
πΏπΌ is the lexical item (which may be empty if there is a lexical gap), ππΆ is the superclass, π΄π is
the axiom pattern, πΌ ππ is the set of individual assertions, and π΄ππ is the set of annotations in
OWL. π΄π is a 2-tuple < π΄ππΆ, π΄π₯ > where π΄ππΆ is the set of axiom pattern classnames, and π΄π₯
is the set of axioms pertaining to the ontological commitment of each element in π΄ππΆ. Each
element in π΄ππΆ is subsumed by ππΆ.
When comparing a concept between a source and target viewpoint, a source and target
concept space is paired. A paired concept space is a 3-tuple PCS = < πΆπ, πΆπ β² , PVP > where πΆπ
is the source concept space, πΆπ β² is the target concept space, and πΆπ β πΆπ β² . PVP is the paired
viewpoint within which πΆπ and πΆπ β² is considered. This is typically the viewpoint from πΆπ β² , but
it can also be an alternative viewpoint. A πΆπ is visualised in Figure 1 for the concept of βriverβ,
shown for two viewpoints: English and French.
4. Abstraction of a Concept Space to be Paired
For an is-a relation in an ontology, the subject or object has two parts: the axiom pattern
(AP), and the superclass(es) (SC) of AP, where both are part of the ontologyβs TBox. To aid the
Figure 1: The parts of a source and target concept space πΆπ and πΆπ β² . ππΆ is the superclass, π΄π is the
axiom pattern, consisting of a set of axiom pattern class names, π΄ππΆ, and axioms π΄π₯ for the ontological
commitment of π΄ππΆ. Ind is the set of individuals asserted for each element in π΄ππΆ. π΄ππ is the set of
annotations for π΄π. The concept space for the concept lexicalised as βriverβ is given for two viewpoints:
English and French.
Table 2
Abstractions of a superclass using the tree from Figure 2.
Source Target Source Target
SC1 π΄π β
β π΄β²π SC2 owl βΆ Thing β
β owl βΆ Thing
SC3 π΄1 β
β π΄β²π SC4 π΄π β
β π΄β²0
SC5 π΄0 β
β π΅0β² SC6 π΄1 β
β π΅πβ²
SC7 π΄1 β
β π΅0β² SC8 π΄0 β
β owl βΆ Thing
decisionmaking process regarding the mismatches (if any), patterns were first identified for
SC and AP. As a starting point, the superclass hierarchy of an ontology was abstracted to a
tree, shown in Figure 2. The red shades are of the source ontology, and the green shades of the
target ontology, where it is assumed that both ontologies are homogenous. Using this tree as a
guide, the abstractions for a superclass are given in Table 2 for SC1βSC8.
Figure 2: The superclass is abstracted to a tree, for both the source and target.
4.1. Superclass Patterns
From the superclass abstractions of SC1βSC8, superclass patterns were identified, given in
P-SC1βP-SC5. In each pattern, the pattern name is given, as well as the possible pattern element
(ππΈ) variations, according to the tree of Figure 2. The superclass pattern is a sub-pattern, to be
considered in conjunction with the patterns for AP. For SC1, P-SC1 applies, for SC2, P-SC5
applies, for SC3, P-SC2 applies, for SC4, P-SC3 applies, for SC5βSC7, P-SC4 applies, and lastly,
for SC8, P-SC3 applies. For the abstraction SC8, where each has owl βΆ Thing as a source
and/or target, this means that there is no corresponding superclass, and the axiom pattern is a
sub-class of owl βΆ Thing . To localise the concept from the source to the target, refactoring
actions are also proposed for selected patterns.
P-SC1: Equal source and target superclass
β’ alignment pattern name: sc-equal
β’ pattern element variations:
1. πππ = {π΄0 }, π ππ = {π΄β²0 }
2. πππ = {π΄π }, π ππ = {π΄β²π }, where π β₯ 1
3. πππ = {π΄π.π }, π ππ = {π΄β²π.π }, where π, π β₯ 1
β’ equality of PE: πππ β‘ π ππ
β’ refactoring required: none
P-SC2: Unequal source and target superclass at same depth, and shared parent
β’ alignment pattern name: sc-unequal-sameDepth-sharedParent
β’ pattern element variations:
1. πππ = {π΄π }, π ππ = {π΄β²π }, where π, π β 0 and π β π
2. πππ = {π΄π.π1 }, π ππ = {π΄β²π.π2 }, where π β 0 and π1 β π2
β’ equality of PE: πππ β’ π ππ
β’ refactoring required: this requires two steps:
1. Refactor the superclass of the πππ π΄π to that of the π ππ π΄π.
2. If the πππ superclass no longer has any sub-classes, then remove it.
P-SC3: Unequal source and target superclass at different depth, and shared parent
β’ alignment pattern name: sc-unequal-differentDepth-sharedParent
β’ pattern element variations:
1. πππ = {π΄π }, π ππ = {π΄β²0 }, where π β 0
2. πππ = {π΄π.π }, π ππ = {π΄β²π }, where π β 0 and π β₯ 1
3. πππ = {π΄π.π }, π ππ = {π΄β²0 }, where π β 0 and π β₯ 1
4. πππ = {π΄π.π }, π ππ = {owl βΆ Thing }, where π, π β₯ 0
5. Same as (1)β(4), but mirrored
β’ equality of PE: πππ β’ π ππ
β’ refactoring required: for the πππ or π ππ with the least depth, this is possibly a lexical gap.
Options include:
1. Add a pseudo-class as a translation of the opposite superclass.
2. Remove the extra classes, taking care to refactor any subclasses and individuals.
P-SC4: Unequal source and target superclass, and no shared parent
β’ alignment pattern name: sc-unequal-noSharedParent
β’ pattern element variations:
1. πππ = {π΄π }, π ππ = {π΅πβ² }, where π, π β₯ 0
β’ equality of PE: πππ β’ π ππ
P-SC5: No source and target superclass
β’ alignment pattern name: sc-none
β’ pattern element variations:
1. πππ = {owl βΆ Thing }, π ππ = {owl βΆ Thing }
β’ equality of PE: β
4.2. Axiom Patterns
The abstractions for an axiom pattern were similarly done as that for a superclass, shown in
Figure 3, with red shades for the source ontology, and green shades for the target. Using this
tree as a guide, the abstractions for an axiom pattern are given in Table 3 for AP1βAP6.
From the abstractions of AP1βAP6, axiom patterns were identified, given in P-AP1βP-AP4.
For AP1, P-AP1 and P-AP2 applies. AP2, AP4, and AP6 are P-AP4. AP3 and AP5 are P-AP3.
Due to the possible permutations of the OWL constructors, to simplify the list of pattern
element variations in each P-AP*, symbols are used for each. The symbol β is used to represent
πΆ with no constructor or with Β¬, the symbol is used to represent πΆ for which there is a
universal or existential restriction, and the symbol β‘ is used to represent intersection and union.
Figure 3: The axiom pattern is abstracted to a tree, for both the source and target. The superclass is at
depth (x).
Table 3
Abstractions of an axiom pattern using the tree from Figure 3.
Source Target Source Target
AP1 πΆ β
β πΆβ² AP2 πΆ β
β π·β²
β² β²
AP3 πΆ β
β πΆ βπ· AP4 πΆ β
β π·β² β πΈβ²
AP5 πΆ βπ· β
β π·β² β πΈβ² AP6 πΆ βπ· β
β πΈβ² β πΉ β²
P-AP1: Equal source and target axiom pattern, same superclass
β’ alignment pattern name: ap-equal-sameSuperclass
β’ pattern element variations:
1. πππ = {βπΆ}, π ππ = {βπΆ β² }, where β is the same for πππ and π ππ
2. πππ = {π
π₯ .πΆ}, π ππ = {π
π₯ .πΆ β² }, where and π₯ are each the same for πππ and π ππ
3. πππ = {πΆ β‘ π·}, π ππ = {πΆ β² β‘ π· β² }, where β‘ is the same for πππ and π ππ
β’ superclass pattern variations: P-SC1, P-SC3
β’ equality of PE: πππ β‘ π ππ
β’ refactoring required: none
P-AP2: Equal source and target axiom pattern, different superclass
β’ alignment pattern name: ap-equal-differentSuperclass
β’ pattern element variations:
1. Same as that for P-AP1
β’ superclass pattern variations: P-SC2, P-SC3
β’ equality of PE: πππ β‘ π ππ
β’ refactoring required: as per the superclass
P-AP3: Unequal source and target axiom pattern, some shared classes
β’ alignment pattern name: ap-unequal-someSharedClasses
β’ pattern element variations:
1. πππ = {βπΆ}, π ππ = {πΆ β² β‘ π· β² }
2. πππ = {πΆ β‘ π·}, π ππ = {π· β² β‘ πΈ β² }
β’ superclass pattern variations: P-SC1, P-SC2, P-SC3
β’ equality of PE: πππ β‘ π ππ
β’ refactoring required: this requires two steps:
1. Remove the πππ classes that are not shared.
2. If there were any individuals asserted for those removed classes, then create a temporary
class and assert those individuals to this new class.
P-AP4: Unequal source and target axiom pattern, no shared classes
β’ alignment pattern name: ap-unequal-noSharedClasses
β’ pattern element variations:
1. πππ = {βπΆ}, π ππ = {βπ· β² },
2. πππ = {βπΆ}, π ππ = {π· β² β‘ πΈ β² }
3. πππ = {πΆ β‘ π·}, π ππ = {πΈ β² β‘ πΉ β² }
β’ superclass pattern variations: P-SC1, P-SC2, P-SC3, P-SC4
β’ equality of PE: πππ β‘ π ππ
β’ refactoring required: this requires two steps:
1. Remove the πππ classes.
2. If there were any individuals asserted for those removed classes, then create a temporary
class and assert those individuals to this new class.
Figure 4: The possible variations of the content of one or more labels of an axiom pattern.
4.3. Annotation Patterns
The abstractions for an annotation is shown in Figure 4, again with red for the source, and green
for the target. The content of a source annotation (using rdfs βΆ label ) can be a lexicalisation
or some other lexical phrase. For the target annotation, if there is no lexicalisation, then
there can be a metaphrase or paraphrase of the source annotation, or similarly an explanation
thereof. A metaphrase is a word-for-word translation, and a paraphrase is a rewording of a
metaphrase. Either of these would be expected for linguistic gaps, with an explanation for
referential gaps, where an explanation would be more detailed than a metaphrase or paraphrase.
Alternatively, a loanword or phrase can be used from the source language, or another language
altogether. If there is no annotation at all for the source, then it is assumed that the URI
fragment is descriptive, and meaning can be derived from it (by a human). A decision tree
diagram for the selection of an appropriate annotation is given in Figure 5, reproduced from [11].
Figure 5: The decision tree diagram for determining the style of annotation to use when dealing with
lexical gaps for a target viewpoint. Each green block is the proposed style to use for that question-answer
selection.
P-Ann1: Both source and target have a label of similar content
β’ alignment pattern name: ann-equal-annotation
β’ pattern element variations:
1. πππ = lexicalisation, π ππ = lexicalisation
2. πππ = meta/paraphrase, π ππ = meta/paraphrase
3. πππ = explanation, π ππ = explanation
P-Ann2: Both source and target do not have a label
β’ alignment pattern name: ann-equal-noAnnotation
P-Ann3: Both source and target do not have a label of similar content
β’ alignment pattern name: ann-unequal-annotation
β’ pattern element variations:
1. πππ = lexicalisation, π ππ = meta/paraphrase
2. πππ = lexicalisation, π ππ = explanation
P-Ann4: Target uses the source label
β’ alignment pattern name: ann-shared-source
β’ pattern element variations:
1. πππ = lexicalisation, π ππ = lexicalisation from πππ
P-Ann5: Both source and target use a lexicalisation from another language
β’ alignment pattern name: ann-shared-external
β’ pattern element variations:
1. πππ = lexicalisation of another language, π ππ = lexicalisation from πππ
5. Discussion
Figure 6: Comparison of each of the use cases, using the tree layout from Figures 2 and 3, where the
named classes and properties are each normalised to an opaque identifier.
Revisiting the use cases, each can be mapped on the tree from Figures 2β3, and the applicable
pattern identified. The mapping of each is given in Figure 6. UC1 is P-AP1 and P-SC1, with UC2
being P-AP2 and P-SC3. UC3 is P-AP-4 and P-SC1. UC4 is both AP-3 and P-SC1. The annotation
pattern for each use case is assumed to be P-Ann1.
The use cases are all real-world examples of ontology localisation differences for a natural
language viewpoint. The patterns identified for the axiom patterns assumed the source and target
classes were leaf nodes in a branch (that is, there are no further sub-classes). It is expected that
additional patterns will be identified so as to represent a use case such as that for βtraditional
healerβ, where for the country South Africa, which would be a region-specific viewpoint,
there are further sub-classes to the same concept. For the annotations, the different βstyleβ of
annotation has been considered as this would be relevant in scenarios such as verbalisation or
when an ontology is used for question-answering. A metaphrase would verbalise differently to
that of a lexicalisation, where it is expected for the former, the generated sentence would be less
readable. To deal with those concepts which do not have a lexicalisation in a target viewpoint,
a decision tree diagram is given in Figure 5, so as to guide the end-user regarding the style of
annotation required.
The benefits of ontology localisation is that an existing ontology can be reused, with the
conceptual differences modelled for the target language (where necessary), and then a new
ontology generated on the fly. Only a small selection of use cases were included here to
demonstrate the variations. The evaluation of a wider variety of use cases is in progress, along
with the algorithms to transform a concept in a source ontology to a target viewpoint. The
focus in this paper has only been on is-a relations, however, part-of will be be addressed in
future work. The focus has also been on a bottom-up approach for the axioms, so top-down
will also be considered in future work. The patterns identified here differ to typical ontology
design patterns in that they are not intended to guide design decisions but rather to document
the possible pattern combinations when localising a concept to a target viewpoint. Once the
evaluation of the localisation process is complete, it is expected that transformation patterns
will be proposed for the re-engineering category of the Ontology Design Patterns repository,
where the focus will be on homogenous sources.
6. Conclusion
A concept space within an ontology has been deconstructed to two parts, with each part
abstracted to several patterns, and the pattern element variations given for each as well. By
comparing each abstracted part, this serves to guide any refactoring that is required when
localising an ontology from a source to a target viewpoint. The focus has been on the adaptation
of the underlying axioms to a specific viewpoint, as an alternative to the labels approach for
ontology multilingualisation, which only affects the annotation layer of the ontology, with the
semantic layer typically remaining unchanged.
Acknowledgments
This research was financially supported by (1) Hasso Plattner Institute for Digital Engineering
through the HPI Research School at UCT, and (2) KNoWS (IDLab), Ghent University and imec
through their Women in Tech - conference sponsorship.
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