=Paper=
{{Paper
|id=Vol-2949/paper7
|storemode=property
|title=Modelling Lexicographic Resources using CIDOC-CRM, FRBRoo and Ontolex-Lemon
|pdfUrl=https://ceur-ws.org/Vol-2949/paper7.pdf
|volume=Vol-2949
|authors=Fahad Khan,Ana Salgado
|dblpUrl=https://dblp.org/rec/conf/swodch/KhanS21
}}
==Modelling Lexicographic Resources using CIDOC-CRM, FRBRoo and Ontolex-Lemon==
https://ceur-ws.org/Vol-2949/paper7.pdf
Modelling Lexicographic Resources using
CIDOC-CRM, FRBRoo and Ontolex-Lemon
Fahad Khan1[0000−0002−1551−7438] and Ana Salgado2,3[0000−0002−6670−3564]
1
Istituto Di Linguistica Computazionale ‘A. Zampolli’, Italy
2
NOVA CLUNL, Centro de Linguı́stica da Universidade NOVA de Lisboa, Portugal
3
Academia das Ciências de Lisboa, Portugal
fahad.khan@ilc.cnr.it, anacastrosalgado@gmail.com
Abstract. The article describes a new approach to the modelling and
publication of lexicographic resources, including retro-digitised dictio-
naries, as linked data. This approach is based on the use of the CIDOC-
CRM aligned FRBRoo ontology together with the Ontolex-Lemon vo-
cabulary and its follow up lexicographic module, lexicog. After introduc-
ing the TEI-based distinction between different views on lexicographic
resources, we discuss Ontolex-Lemon and CIDOC-CRM and FRBRoo.
Next, we look at some motivating use cases before introducing our ap-
proach. Finally, we model one of these use cases in more depth using this
approach.
Keywords: data modelling · dictionary · linked data · tei · lexicographic
resources
1 Introduction
In this article, we propose an approach to the modelling and publication of retro-
digitised dictionaries (and related resources) as linked data based on the use of
the CIDOC Conceptual Reference Model (CIDOC-CRM) aligned FRBRoo on-
tology together with the Ontolex-Lemon vocabulary and its follow-up module,
lexicog (all introduced in Section 2). To this end, we propose several new classes
and properties to act as a bridge between the first resource and the latter two
vocabularies (see Section 4). We motivate our approach in Section 2.1 by exam-
ining the different levels of descriptions that lexicographic works usually contain
and that this approach is specifically designed to model. Our analysis is based
on several representative use cases (presented in Section 3).
2 Background
2.1 Different Views on Lexicographic Resources
This article discusses the modelling of lexicographic resources as linked data.
The term lexicographic resources includes so-called machine-readable dictionar-
ies [6], that is, lexical resources that are either digital versions of previously
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
2 Khan and Salgado
existing paper dictionaries (so-called retro-digitised dictionaries) or updated dig-
ital editions of such dictionaries. In addition, we also classify as lexicographic
resources born-digital lexical resources which are structured or organised accord-
ing to the conventions of traditional dictionaries (or which contain significant
portions organised as such). In broader terms, we define a lexicographic resource
as a language resource a significant portion of which consists of a set of lexi-
cographic articles or entries written and organised according to some suitable
lexicographic criteria. Prima facie, there are two main approaches which we can
take in modelling lexicographic resources in general and retro-digitised dictio-
naries in particular. We can view a dictionary primarily as a cultural/textual
artefact with its own specific publishing history and its own verbal expression
and visual arrangement of the linguistic content contained within it. Or we can
prioritise the linguistic content instead, ignoring how it is presented and the ex-
act sequence of words used in, for example, the definitions of articles. Of course,
there is also a third (potentially more verbose) approach, that is, to do both
at the same time and make sure both kinds of information are aligned. The
distinction made in Chapter 9 (“Dictionaries”) of the Text Encoding Guidelines
(TEI) between the typographical, editorial and lexical views of a dictionary is
particularly useful in this discussion. These views are defined as follows: the ty-
pographical view concerns the layout of individual pages; the editorial view is
concerned with the properties of a text modelled as a sequence of tokens; and
the lexical view is concerned with the conceptual or linguistic content of a dictio-
nary as a whole, as well as its individual entries1 . In addition to these views, TEI
also offers extensive provision, in the teiHeader element, for including metadata
about an original resource to be modelled (e.g., who the authors were and when
it was published) and about the process of its digitisation as well as the curation
of that process.
In this work, we will concentrate on the modelling of lexicographic resources
as linked data to be published on the Semantic Web. This means that the dis-
tinctions made in the TEI guidelines are not sufficient for our purposes since the
data modelling affordances that TEI-XML offers are different from those offered
by linked data and the Semantic Web. This is due both to the expressive possi-
bilities offered by knowledge representation formalisms, such as the Resource
Data Framework (RDF) and the Web Ontology Language (OWL), as
well as to the ready availability of tools and relevant ontologies and vocabularies
in those formalisms. All of this offers data modellers enhanced possibilities for
representing the conceptual relationships between different levels of description
in lexicographic resources in a more explicit and, hence, more machine-actionable
way. Provision for the modelling of the TEI lexical view in a linked data lexico-
1
With the typographical view we would be interested in e.g., the position of line breaks
in a text, or the visual arrangement of entries on any single page; with the editorial
view we are interested in such things as which words are used in the description of the
article and in which order; finally in the lexical view we’re interested in information
about the given domain of a specialised lexical unit (a term) or sense of a lexical
unit, or that a given headword is a “verb”.
Modelling Lexicographic Resources 3
graphic resource is already covered in large part by Ontolex-Lemon (which is
explored in some detail in Section 2.2) with the latter providing a common data
model for the creation of linked data lexicons. Such lexicons can potentially
contain information derived from various sources (including, but not limited
to, lexicographic resources), and so the use of a common model that imposes
conceptual restrictions on how lexical data is represented ultimately facilitates
the interoperability of such datasets. Such lexicons can potentially contain in-
formation derived from various different sources (including but not limited to
lexicographic resources) and so the use of a common model that imposes con-
ceptual restrictions on how lexical data is represented2 ultimately facilitates the
interoperability of such datasets. However, this presents us with the problem of
translating the surface representation of this same information (this represen-
tation corresponding to the typographic and editorial views mentioned above)
into linked data as well as of relating these different levels of description to-
gether with each other and with the lexical view – something that is not always
straightforward3 . We are often also interested in numerous other details related
to the conception and publication of dictionaries. This is true of historically
important dictionaries and/or dictionaries that have gone through significant
changes across their different editions.
Broadly speaking then, we are talking here about the distinction between
the linguistic content of a dictionary text and its metadata (we would argue
that the latter category also includes the TEI typographic and editorial views).
Our approach (which as far as we are aware is fairly novel with regard to lex-
icographic resources) is to model this complexity using the top-level ontology
CIDOC-CRM and the CIDOC-aligned ontology FRBRoo along with Ontolex-
Lemon and lexicog. In other words, we first seek to model separately and then to
integrate together the various different aspects of a lexicographic resource using a
conceptual modelling approach that uses the ontologies/vocabularies mentioned
above along with various others available on the Semantic Web. We propose to
do this in a way that makes the semantics of the data as explicit and there-
fore as machine-actionable as possible. To motivate our approach (described in
more detail in Section 4) and to help the reader better appreciate some of the
challenges involved, we will look at one specific kind of use case in detail (in
Section 3). Before that, however, we will provide brief introductions to some of
the models and vocabularies just mentioned.
2
For instance a headword, corresponding to a lexical entry in Ontolex-Lemon, can
only be associated with a single part of speech
3
At the same time however our task is simplified by the fact that the information in a
dictionary (as represented by the lexical view) often (though not always) tends to be
of a standard form and is organised in a fairly standardised way. At least this is the
case to an extent which isn’t true of for example biographies or scientific papers, or
most other kinds of text where we might be interested in annotating both structural
aspects as well as the truth values of the claims being made in the text.
4 Khan and Salgado
2.2 Ontolex-Lemon and Lexicog
The Lexicon Model for Ontologies of the W3C Ontology-Lexicon com-
munity group, also known as Ontolex-Lemon, is a linked data native model
for the creation of lexical resources4 [9]. Ontolex-Lemon was originally developed
to facilitate the addition of linguistic information to Semantic Web ontologies.
However, it quickly became a de facto standard for the modelling of lexicons
in RDF with or without the ontological component as originally envisaged. In-
spired by previous lexical standards such as the Lexical Markup Framework [6],
the vocabulary contains classes such as Lexicon, Lexical Entry, Lexical Sense,
Form and Word (the names of the classes are fairly self-explanatory).The pop-
ularity of the Ontolex-Lemon vocabulary as well as the need to deal with cases
where there is a substantial difference between the organisation of entries on a
page and the modelling proposed by Ontolex-Lemon5 , soon led to calls for an
extension of the original model specifically tailored to the representation of lexi-
cographic resources. The OntoLex Lemon Lexicography Module (lexicog)
was subsequently developed in response [3]. Figure 1 shows the main classes and
properties of the new module. The class Lexicographic Resource, as distinct
from the Lexicon class in Ontolex-Lemon (included in the lime sub-module of
the latter), consists of a collection of Entry individuals where each such individ-
ual is “a structural element that represents a lexicographic article or record as it
is arranged in a source lexicographic resource”6 . The latter is also a subclass of
the class Lexicographic Component whose individuals are defined “structural
element[s]” representing “the (sub-)structures of lexicographic articles providing
information about entries, senses or sub-entries”. Individuals of the class Entry
then are linked to Ontolex-Lemon entries via the entry object property with
the latter relating Entry to “an element that represents the actual information
provided by that component in the lexicographic resource”. The lexicog module
makes a good start at addressing the modelling issues raised by the presence of
different dictionary views as identified by the TEI guidelines. However as we will
argue in Section 3 there are important kinds of information relevant to mod-
elling lexicographic resources which are still not covered by the combination of
Ontolex-Lemon and lexicog.
2.3 CIDOC-CRM and FRBRoo
The CIDOC Conceptual Reference Model (CIDOC-CRM)7 is an event-
based conceptual model or ontology that was initially developed for the cultural
heritage domain with the aim of facilitating the harmonisation and integration
4
The commnunity page is here: https://www.w3.org/community/ontolex/; the guide-
lines can be found here: https://www.w3.org/2016/05/ontolex/.
5
One well-known example of this is when dictionaries violate the Ontolex-Lemon
model constraint requiring one part of speech per Lexical Entry
6
This and the other following lexicog quotes are taken from the guidelines to be found
at https://www.w3.org/2019/09/lexicog/
7
http://www.cidoc-crm.org/
Modelling Lexicographic Resources 5
Fig. 1. the OntoLex Lemon Lexicography Module
of data arising from different cultural heritage organisations [5]. CIDOC-CRM
makes a clear distinction between endurants and perdurants, i.e., between entities
with a persistent identity that may orginate, survive or terminate in events, and
temporal concepts which happen rather than maintaining a stable identity over
time. The Functional Requirements for Bibliographic Records (FRBR)
entity relationship model, instead, was developed by the International Feder-
ation of Library Associations and Institutions as a conceptual model for
the classification of intellectual products in bibliographic databases and library
catalogues [4]. FRBR is well known for its four-fold classification of bibliographic
entities based on the distinction between a Work , an Expression, a Mani-
festation and an Item. The distinction is an extremely pertinent one for our
discussion as we will see in Section 3. We refer in what follows to the version of
this distinction introduced in FRBRoo, an ontology based on FRBR and which
is harmonised with CIDOC-CRM [2]8 :
– F1 Work: “[C]omprises distinct concepts or combinations of concepts iden-
tified in artistic and intellectual expressions [...] The substance of Work is
ideas” [2]. Note that in the case of dictionaries this would encompass the
TEI lexical view;
– F2 Expression: “[C]omprises the intellectual or artistic realisations of works
in the form of identifiable immaterial objects, such as texts, poems [...] or any
combination of such forms that have objectively recognisable structures”[2].
8
Note that FRBRoo class identifiers are prefixed by the letter ‘F’ and a number.
6 Khan and Salgado
In the case of dictionaries we claim that this description encompasses the
TEI editorial view;
– Originally one class in the FRBR model, Manifestation, this latter corre-
sponds to two separate classes in FRBRoo: F3 Manifestation Product Type
and F4 Manifestation Singleton. The former class is defined as “[defining]
all of the features or traits that instances of F5 Item normally display in
order that they may be recognised as copies of a particular publication” [2];
the latter as “[comprising] physical objects that each carry an instance of F2
Expression, and that were produced as unique objects” [2]. In the case of dic-
tionaries F3 Manifestation Product Type encompasses the TEI typographic
view;
– F5 Item: “[C]omprises physical objects” [2] such as specific physical copies of
dictionaries kept at libraries or academic institutions. This class is associated
with the kind of metadata information that is usually contained within the
TEI header element.
These FRBRoo classes (together with various others included in the ontology)
are ideal for describing those aspects of a lexicographic resource which we men-
tioned above and which are related to its metadata (including those described
in the typographical and lexical views). For instance, we can classify a multi-
edition dictionary as a F15 Complex Work with its different individual editions
classed as instances of F15 Individual Work. Each of these different editions can
then be described at the level of F2 Expression in order to specify, for example,
the wording of individual entries. Moreover we can also describe the dictionary
at the level of F3 Manifestation Product Type in order to specify the con-
tent and placement of images and their relation to the text (this is important
in the case of illustrated dictionaries). FRBRoo also allows for the modelling
of dictionaries which have been translated from one language to another (such
as, for instance, the well-known Liddell-Scott-Jones Ancient Greek-English lexi-
con which has been translated into several languages, including Modern Greek).
Other than these benefits however, the use of an ontology to model a lexico-
graphic resource, and especially one that has been aligned with a popular top
level ontology (CIDOC-CRM in this case), helps to improve the interoperability
and re-usability of the resulting dataset.
Once more it is worth emphasising that dictionaries are both cultural arte-
facts and highly structured descriptions of linguistic phenomena. It is also worth
underlining the importance of being able to relate together these aspects of the
same resource. It would be useful, for instance, to be able to represent how lex-
ical content changes over time and across different editions of a dictionary. We
should also be able to describe the linguistic claims that a dictionary makes
regardless of their truth or falsity (this latter as determined by an editor or
curator). This would allow for a higher level description of such claims as well
as for the integration of data from different lexical and lexicographic sources
and/or the enrichment of single lexicographic resources. In order to further de-
velop this point, and to look at these issues in more detail, we will look at a
specific kind of lexicographic data in the next section, namely lexicographic ci-
Modelling Lexicographic Resources 7
tations/attestations; this will also provide us with a use case which we will model
below using our new approach.
3 Citations and Attestations
Dictionaries very often contain citations under individual entries. This is es-
pecially typical of scholarly or encyclopedic dictionaries such as the Oxford En-
glish Dictionary or the Liddell-Scott-Jones Ancient Greek-English Lexicon (LSJ)
where individual lexicographic articles are often associated with large numbers
of them. Here citations are usually meant to show that a lexical unit, a sense,
a form, or indeed any kind of linguistic phenomena is attested to in a given
text or corpus. Of course these purported citations can sometimes be erroneous.
This is the case, for example, where the text cited doesn’t actually attest to
the phenomenon described due to a misreading of the source or even where it
doesn’t exist in the form which the lexicographer hypothesised. In these kinds
of situation we are concerned with facts that pertain to (at least) two different
levels of description: facts at the level of the work (a dictionary citing a text as
attesting a given phenomenon) and at the level of content (the phenomenon is
or is not attested to by the text in question).
A couple of examples may be useful here; both of them are taken from [7]9 .
In that article we looked at one of the citations in the LSJ lexicographic article
for the word ἀνώμᾰλος (anomalos). This citation was marked by the abbrevia-
tion ‘cj’10 and referred to a critical reconstruction of a work based on existing
fragments. The citation was subsequently excluded from another Ancient Greek
dictionary which was closely based on the LSJ thus hinting at its unreliability. We
can therefore describe this citation both according to which dictionaries or lexi-
cal resources it is found in (and which editions) as well as whether the cited text
really attests to the phenomenon in question. The other example concerns the
Italian verb riprovare which means both11 ‘to try something again’12 and ‘to
scold, rebuke’. The well known Italian language dictionary Il vocabolario Treccani
[10] (which we will refer to as Treccani from hereon in) lists these as two separate
lexicographic articles: riprovare1 (‘to try again’) and riprovare2 (‘to scold’)13
The entry for riprovare1 cites the motto of the short lived but highly influential
16th scientific society L’Accademia del Cimento (AdC), i.e., provando e
9
Note that although we presented these same examples in that article our approach
was different in that work and didn’t involve the use of CIDOC-CRM or any upper
ontology for structuring information at different levels of description.
10
This is usually found in critical apparatuses and stands for the Latin conicit ‘con-
jectures’.
11
In fact we are dealing with two homonymous lexical units here.
12
Deriving in this instance from provare which has the meaning of ‘to try’ and the
prefix ri- which adds the sense of repetition.
13
See the online version of both entries can be
found here:https://www.treccani.it/vocabolario/riprovare1/,
https://www.treccani.it/vocabolario/riprovare2/
8 Khan and Salgado
riprovando (‘trying and trying again’). This motto derives from a terzina of
Dante’s Divine Comedy (Par. III, 1-3): “Quel sol che pria d’amor mi scaldò ’l
petto,/di bella verità m’avea scoverto,/provando e riprovando, il dolce aspetto”.
The Treccani entry however directs the dictionary user to the second sense of
the entry for the verb provare 14 ‘to try’. There it is explained that although the
motto derives from Par. III, 1-3, it does not, in fact, mean the same thing as
it does in the latter15 . The Treccani entry for riprovare2 also cites the use of
the gerund form riprovando in the same passage of Dante, but in this case it
is as an actual attestation. Another authoritative Italian language dictionary,
Il Grande Dizionario della Lingua Italiana (GDLL) [1]16 , cites both the motto
of L’Accademia del Cimento and the same terzina from the Divine Comedy in
its entry for riprovare1 (and note that riprovare1 and riprovare2 in the GDLL
are synonymous with riprovare1 and riprovare2 respectively in Treccani). The
GDLL makes the contradictory claim, however, that both cited texts (the AdC
motto and the terzina from the Paradiso) do attest to the entry in question,
namely riprovare1 .
We can summarise the claims made above in the following series of state-
ments: (1) Treccani’s entry for riprovare1 cites the AdC motto; (2) Treccani’s
entry for riprovare2 cites Par. III, 1-3 as an attestation; (3) GDLL’s entry for
riprovare1 cites Par. III, 1-3 and the AdC mottos as attestations; (4) riprovare2
is attested by the AdC motto and Par. III, 1-3; (5) riprovare1 is attested by the
AdC motto but not Par. III, 1-3.
The first three items in the list are true statements about the lexicons which
they refer to; they describe the existence of three successful citational (‘perfor-
mative’) speech acts. These statements only deal indirectly with lexical units
or their usages. Rather, they are primarily concerned with documents or works
and the rhetorical/organizational structure pertaining to them. The other two
statements, those which we have numbered 4 and 5, instead, describe the direct
relationship between an item in a lexicon and a text which evidences it, or bet-
ter, attests to its past use. The fourth statement is false but the fifth one is true.
However in neither case does this follow from the truth (or falsity) of the first
three statements. Statements 4 and 5 are at the level of content whereas the
first three are at the level of metadata. If we were to create a linked data lexico-
graphic resource combining the two dictionaries listed above (based on a lexical
view) and including relevant metadata information about the original sources,
then we could include 1,2,3, and 5 as RDF statements but adding 5 would be to
add a false statement. In case we are not sure about which claims are true (or
which claims we would endorse by creating a curated version of the information
in these dictionaries) then we should stay at the metadata level. We could also
14
https://www.treccani.it/vocabolario/provare/
15
“[M]otto assunto verso il 1666 dall’Accademia fiorentina del Cimento, tratto dalla
Divina Commedia (Par. III, 3, dove però significa ‘approvando e disapprovando’)”
16
The dictionary is consultable here as a collection of scans:
http://www.gdli.it/; the image of the page for riprovare can be found here:
http://www.gdli.it/JPG/GDLI16/00727.jpg.
Modelling Lexicographic Resources 9
publish the GDLL with provenance metadata of course without curating it, or
publish separate statements about single entries or senses as nanopublications
[8] but it would also be useful to have the possibility of putting together a single
dataset in a way that integrates together this information deriving from multiple
sources. In the next section, we look at how we can model this situation with
linked data ontologies.
4 Our Proposed Approach
Our intention is to create bridges between the models and vocabularies which
we have mentioned so far. We do this through the definition of a number of
new classes and properties. This should facilitate a more extensive modelling of
lexicographic resources and at multiple levels of description. The first of these
new classes is Lexicographic Work. This is a subclass of both the FRBRoo class
F1 Work and the Ontolex-Lemon class Lexicon. We define it as follows: Lexi-
cographic Work comprises concepts or combinations of concepts for represent-
ing/describing the lexicon for a given language community or communities or
domain. As F1 Work is a subclass of the CIDOC-CRM class E89 Propositional
Object17 we can view individuals of Lexicographic Work as sets of propositions
about lexemes and related linguistic concepts belonging to a lexicon. The second
new class that we introduce is Lexicographic Expression. This is a subclass of
the FRBRoo class F2 Expression and the Ontolex-Lemon class Lexicographic
Resource. We define it as follows: Lexicographic Expression comprises an in-
tellectual realisation of the description of a lexicon as a structured text. In other
words, it is a text viewed apart from a specific typographic realisation: a se-
quence of words that has an additional organisation in terms of entries, senses
(defined as a sub-part of a lexicographical article that discusses a meaning of
a lexical unit), forms, etc. By making these newly defined classes subclasses of
both Ontolex-Lemon and FRBRoo classes we can take advantage of properties
and related classes from both ontologies (as well as from the CIDOC-CRM).
Moreover, and analogously to the lexicog class Entry, we propose the creation of
new subclasses of the lexicog class Lexicographic Component, corresponding to
senses and forms as parts of a typographical view. We also envision an extensive
use of other Semantic Web ontologies for provenance metadata and in particu-
lar of Prov-O 18 and the Citation Typology Ontology (CiTO) (part of the
SPAR suite of ontologies for the publishing domain19 ). We use the object prop-
erty cites from the latter ontology in our example below20 . This property has a
series of sub-properties corresponding to different kinds of citations (e.g., cites
17
Defined as “sets of propositions about real or mental things and that are documented
as single units or serve as topic of discourse”[2].
18
https://www.w3.org/TR/prov-o/
19
http://www.sparontologies.net/
20
This property applies to cases where “[t]he citing entity cites the cited entity, ei-
ther directly and explicitly (as in the reference list of a journal article), indirectly
(e.g. by citing a more recent paper by the same group on the same topic), or im-
10 Khan and Salgado
as source document, cites for information). We have decided to define another
subproperty of cites or more precisely of the CiTO property cites as evidence.
This new property is cites as attestation and covers cases where the citing en-
tity cites the cited entity as attesting to the existence of a linguistic phenomenon
or phenomena. We also make use of the new Frequency Attestation and Corpus
Information (FrAC) module being drafted by the W3C OntoLex group in our
example encoding. Although this is still currently under development, the mod-
ule guidelines are in a fairly advanced state of preparation and we have chosen
classes and properties in our example encoding whose definitions are, by now,
stable21 . These include Attestation, representing attestations of linguistic “ob-
servables” in texts and the properties attestation, which relates together these
observables and the texts which attest them, and locus which represents loca-
tions in a text. In the rest of this section we describe our proposal for encoding
the riprovare example from Section 3 using the vocabularies/ontologies we have
mentioned above and the new properties we have just defined. In Figure 2 we
show the two entries both at the level of the Lexicographic Work and at the
level of Lexicographic Expression. We can use OWL the sameAs property to
Fig. 2. Dictionaries
link together t riprovare1 and b riprovare2 (note that we would not do this in
the case of the two Entry individuals te riprovare1 and be riprovare2). In Fig 3
instead we present the relationship between the two entries and the same textual
fragment of Dante’s Divine Comedy (which we have defined as an individual of
the FRBRoo class Expression Fragment) as defined above, using the FRaC class
plicitly (e.g. as in artistic quotations or parodies, or in cases of plagiarism)”. See
https://sparontologies.github.io/cito/current/cito.html
21
The latest draft can be found here: https://github.com/ontolex/frequency-
attestation-corpus-information
Modelling Lexicographic Resources 11
Attestation and the properties attestation and locus. Note that we have only
represented the fact that GDLL cites the Paradiso as an attestation at the level
of Expression and not at the level of Work/Lexicon (although we have done so
in the case of Treccani because of the veridicity of the description of the latter).
Fig. 3. Attestations
5 Further Work
The work which we have described in this article represents an attempt to use
ontologies to model Semantic Web lexicographic resources at different levels
of description. We have found CIDOC-CRM and its aligned/related ontologies
(together with pre-existing lexical ontologies such as Ontolex-Lemon) to be es-
pecially useful in this regard. Indeed we are currently working on representing
linguistic hypotheses (such as etymologies) in lexical resources using CRMSci.
Furthermore, the authors of the current work are involved in the digitization of
three editions of the historically significant Portuguese dictionary, Diccionario
da Lingua Portugueza (Dictionary of the Portuguese Language), by António de
Morais Silva, as part of a Portuguese national project, MORDigital. The inten-
tion is to make these dictionaries made available in both TEI-XML as well as in
linked data. This project will give us the opportunity to apply and to test the
approach which we have proposed in the current article on a large scale, real-life
dataset.
12 Khan and Salgado
Acknowledgements
Supported by the MORDigital – Digitalização do Diccionario da Lingua Por-
tugueza de António de Morais Silva [PTDC/LLT-LIN/6841/2020] project fi-
nanced by Portuguese national funding through the FCT – Fundação para a
Ciência e Tecnologia and the European Union’s Horizon 2020 research and in-
novation programme under grant agreement No 731015 (ELEXIS – European
Lexicographic Infrastructure).
References
1. Battaglia, S.: Il Grande dizionario della lingua italiana di Salvatore Battaglia.
UTET, Torino (1961)
2. Bekiari, C., Doerr, M., Le Boeuf, P., Riva, P., Aalberg, T., Barthélémy, J.,
Boutard, G., Görz, G., Iorizzo, D., Jacob, M., et al.: FRBR object-oriented defini-
tion and mapping from FRBRER, FRAD and FRSAD (Version 3.0) (Sep 2017),
http://www.cidoc-crm.org/frbroo/sites/default/files/FRBRoo V3.0.pdf
3. Bosque-Gil, J., Gracia, J., Montiel-Ponsoda, E.: Towards a Module for Lexicogra-
phy in OntoLex. In: Proceedings of the LDK 2017 Workshops: 1st Workshop on
the OntoLex Model (OntoLex-2017), Shared Task on Translation Inference Across
Dictionaries & Challenges for Wordnets. Galway, Ireland (2017), http://ceur-
ws.org/Vol-1899/
4. Coyle, K.: FRBR, before and after: a look at our bibliographic models. ALA Edi-
tions, an imprint of the American Library Association, Chicago (2016)
5. Doerr, M.: The CIDOC Conceptual Reference Module: An Ontological Approach
to Semantic Interoperability of Metadata. AI Magazine 24(3), 75–75 (Sep 2003)
6. Francopoulo, G. (ed.): LMF lexical markup framework. Computer engineering and
IT series, ISTE Ltd ; John Wiley & Sons, London : Hoboken, NJ (2013), oCLC:
ocn812570948
7. Khan, F., Boschetti, F.: Towards a Representation of Citations in Linked Data Lex-
ical Resources. In: Proceedings of the XVIII EURALEX International Congress:
Lexicography in Global Contexts (2018)
8. Kuhn, T., Banda, J.M., Willighagen, E., Ehrhart, F., Evelo, C., Malas, T.B., Du-
montier, M., Merono-Penuela, A., Malic, A., Poelen, J.H., et al.: Nanopublications:
A growing resource of provenance-centric scientific linked data. In: 2018 IEEE 14th
International Conference on e-Science (e-Science). p. 83–92. IEEE (2018)
9. McCrae, J.P., Bosque-Gil, J., Gracia, J., Buitelaar, P., Cimiano, P.: The Ontolex-
Lemon model: development and applications. In: Proceedings of eLex 2017 confer-
ence. pp. 19–21 (2017)
10. Simone, R. (ed.): Enciclopedia dell’italiano: Il vocabolario Treccani. Ist. della Enci-
clopedia Italiana, Roma, ed. speciale per la libreria edn. (2010), oCLC: 0759152511