=Paper=
{{Paper
|id=Vol-3427/paper3
|storemode=property
|title=Extracting Knowledge-rich Information From Definitions. A Corpus-based Approach to Building a Conceptual-based Terminological Resource
|pdfUrl=https://ceur-ws.org/Vol-3427/paper3.pdf
|volume=Vol-3427
|authors=Margarida Ramos,Rute Costa
|dblpUrl=https://dblp.org/rec/conf/mdtt/RamosC23
}}
==Extracting Knowledge-rich Information From Definitions. A Corpus-based Approach to Building a Conceptual-based Terminological Resource==
https://ceur-ws.org/Vol-3427/paper3.pdf
Extracting knowledge-rich information from definitions. A
corpus-based approach to building a conceptual-based
terminological resource
Margarida Ramos 1, Rute Costa 1
1
NOVA CLUNL, Centro de Linguística da Universidade NOVA de Lisboa, Avenida de Berna 26-C, 1069-061
Lisboa, Portugal
Abstract
This paper aims to describe a text-mining approach on a domain corpus (cork) within the theoretical
framework of the dual dimension of terminology to create a terminological dictionary and correlate
it with an ontology. We will make some considerations on (i) domain specificities; (ii) lexical
markers; (iii) automatic corpus processing using Sketch Engine; (iv) representation of lexical
networks using CmapTools; and (v) representation of the concept system using Protégé. The goal
of the ontology is to logically support the coherence and quality of the natural language definitions
contained in the terminological resource.
Keywords1
terminology; definition; domain-ontology; knowledge-rich information; domain corpus;
terminological dictionary.
1. Introduction
This paper aims to demonstrate a method for developing a terminological dictionary based on a
domain ontology. To this end, we will describe the methods used to capture specialised lexical and
conceptual knowledge from the corpus and use it to develop a dedicated ontology. The terminological
resource will consist of a linguistic description of the specialised concepts, based on the formal
definitions of the concepts that make up the cork ontology, the OntoCork [11].
The method used in this paper is corpus-driven. The corpus was compiled based on rigorous criteria
specific to terminological work [10], where the specialised context of text production is a key-element.
In this sense, the corpus is composed of technical explanatory and normative (standards) texts. For
corpus analysis, we used Sketch Engine2 to find and systematise lexical-semantic relationships. During
the corpus analysis process, we found two types of relevant knowledge-rich information [6]: definitions
and definitional contexts. Definitions are one of the components of the glossary’s microstructure that
can be found at the end of the normative texts. The purpose of these definitions is to achieve a consensus
among the members of the cork community. On the other hand, the definitional contexts are integral
parts of the texts and have relevant specialised lexical-semantic markers in their structure.
Our method encompasses two stages:
(i) From the linguistic analysis of the lexical markers, and the corresponding lexical-semantic
relations observed between the terms, we systematise the results into lexical maps using CmapTools3.
(ii) Based on the previous stage, we proceed to the conceptual analysis and subsequent formal
representation. The conceptual analysis grounds the identification of conceptual relations obtained by
interpreting the lexical-semantic relations observed between two terms. To infer conceptual relations –
2nd International Conference on “Multilingual Digital Terminology Today. Design, Representation Formats and Management Systems”
(MDTT 2023), June 29–30, 2023, Lisbon, Portugal
EMAIL: mvramos@fcsh.unl.pt (M. Ramos); rute.costa@fcsh.unl.pt (R. Costa).
ORCID: 0000-0001-7209-3806 (M. Ramos); 0000-0002-3452-7228 (R. Costa)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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CEUR Workshop Proceedings (CEUR-WS.org)
2
https://www.sketchengine.eu/
3
https://cmap.ihmc.us/
1
such as the associative type – and to identify characteristics that will help us in the process of building
concept systems, we resort to deductive mechanisms employing the Aristotelian formula: X=Y+DC to
build OntoCork.
2. Domain corpus: cork
The Cork Corpus was built up from texts produced within the cork industry. The internal and
external criteria [1],[8] used to build our specific-domain corpus are systematised in Table 1.
Error! Reference source not found. 1
Internal and external criteria of the cork corpus
Criteria Purpose/description
Degree of specialisation Produced by experts and semi-experts
Source validation Entities recognised as an authority
Type Technical-explanatory; normative
Content adequacy On cork/Cork stopper
Synchronism (≤ 10 years) Given the fast evolution of technology
The corpus comprises 98 texts written in European Portuguese (see Figure 1).
newsletters specialised books instruction manuals
corpora collection
brochures 18% periodicals 8% 4%
industrial guide
6% 4%
studies 1%
7% reports standards
8% decree-laws 9%
academic articles theses 6%
16% 13%
Figure 1: Corpora collection
These texts were produced by experts from different organisations and in different domains related
to the cork industry. The texts were collected according to the following criteria: (1) texts produced by
and for the scientific community in the domain of cork; (2) texts produced by experts for quasi-experts;
and (3) texts produced by experts for non-experts.
3. Terminological data extraction
Considering the 98 documents of the corpus, we have obtained the quantitative data shown in Table
2.
Error! Reference source not found. 2
Quantitative data of the corpus
Total number
Tokens 1,712,652
Words 1,217,968
Sentences 48,031
For the corpus exploration and linguistic data analysis, we mainly focused on 43 texts produced in
two communicative settings, namely (i) expert–semi-expert, and (ii) expert–quasi-
experts/professionals, while the remaining 55 texts were used as a reference corpus [2] so that we could
compare a given terminological data extraction (see Figure 2). The corpus was processed using Sketch
Engine, with which we compiled, annotated, and queried the corpus employing advanced searches in
Corpus Query Language (CQL) format, where regular expressions (regex) are applied.
2
Scientific : expert-expert Communicative setting of text production
Regulatory: semi-expert - expert 4
6
Marketing : semi-expert - non-expert 16
16
Narrative-Informative : semi-expert - non-expert 43
27
29
Economics : expert- semi-expert
Technical-explanatory & normative : expert -
quasi-expert / professional n=98
Error! Reference source not found. 2: Subcorpus under focus (43 texts) based on the communicative
setting of text production
Among the results presented in Table 2, the most frequent terms are “cortiça” [cork]4 and “rolha”
[stopper]. Given the high frequency of these two terms, we analysed the contexts in which they occur
in the subcorpus (43 texts) using the Word Sketch function as a first option, with which we identified
some candidate terms such as “ROLHA COLMATADA” [colmated stopper] (in capital letters). We then
moved on to simple queries (concordances) to search for polylexical terms containing adjectives in their
pattern. Once the most common morphosyntactic structures of terms were identified, we decided to
improve our search for terms and definitions employing advanced queries, namely through regex, so
that we could capture knowledge-rich contexts (KRC) [6], e.g., definitions (definitions found in context)
and definitional contexts (contexts explaining what the concept is; thus valuable for understanding
and/or elaborating proper definitions).
3.1. Exploring the corpus with text mining methods
Based on the patterns we have identified within definitional contexts, we explored the subcorpus
with advanced queries using regexes. For this paper, we will highlight two specific regexes that proved
productive in isolating lexical relations between terms, but also in finding definitional contexts where
the generic term is expanded in its syntax (see Table 3).
Error! Reference source not found. 3
Linguistic expressions commonly used by experts
Definitional contexts (pt) Literal translation into English
(1) Rolha que foi submetida a um tratamento químico en: Stopper that was submitted to chemical treatment with
com o objectivo de desinfectar e/ou homogeneizar the aim of disinfecting and/or homogenising the colour and /
a cor e/ou branquear. or bleaching
(2) Rolha cuja superfície lateral foi submetida a uma en: Stopper whose side surface was submitted to an abrasion
operação de abrasão para a tornar cilíndrica ou operation to make it cylindrical or to reduce its diameter.]
diminuir o seu diâmetro.
The first regex has the following structure: "rolha"[tag="V.P.*SF"], whose formulation aims to
match patterns as ONLY forms of “rolha” [stopper] followed by ANY past participle ONLY in the singular
and feminine inflection. For the elaboration of this regex, we considered the linguistic expressions used
repeatedly by the experts, such as the past participle co-occurring with a term (see Table 4). The
outcomes of this query, namely 69 hits, delivered the most productive patterns for identifying lexical
markers, such as “x foi submetida a y” [x was submitted to y], as well as terms whose morphosyntactic
structures fall under our search patterns, such as [Noun + Past Participle], e.g., “x acabada” [finished
X] or “X terminada” [finalised X] where X is a term and Y corresponds to a structure that has proved
4
Our translation
3
to be rich in knowledge information, i.e. information provided by the experts that allows us to perceive
their conceptualisations [9].
Considering the satisfactory results obtained, we decided to expand its formulation (regex 2):
"rolha"[(tag="D.*"|(tag="S.*")]?[tag="A.*"]?"cortiça"?[]{0,4}"rolha"[]{0,4}[tag="V.
P.*SF"]. In this case, we want to match a context in which the terms “rolha” [stopper] and “cortiça”
[cork] may co-occur with either adjectives, past participles, or found duplicated, in addition to the
functional forms. Out of the 55 hits matched by this regex, 48 were either a description or a definition.
From the whole set of descriptions or definitions semi-automatically extracted from the Cork
Corpus, we decided to select ten (10) definitions for linguistic and conceptual analysis (see Table 4).
Error! Reference source not found. 4
Ten (10) definitions to organise a typology of cork stoppers
# 10 definitions (literal translations from pt) 10 definitions (pt) extracted from the Cork Corpus
stopper rolha
Product obtained from natural cork and / or Produto obtido da cortiça natural e / ou de cortiça
1 agglomerated cork, consisting of one or more aglomerada, constituído por uma ou mais peças,
pieces, intended to seal bottles or other containers destinado a vedar garrafas ou outros recipientes e a
and to preserve their contents. (5.1 - NORM) preservar o seu conteúdo. (5.1 - NORM)
STOPPER ROLHA
piece of cork, usually cylindrical, conical or prismatic peça de cortiça, em geral cilíndrica, troncocónica ou
quadrangular, sometimes with rounded or prismática quadrangular, por vezes de arestas
2 chamfered lateral edges, consisting of one or laterais boleadas ou chanfradas, constituída por um
several glued elements and intended to seal the ou vários elementos colados e destinada a vedar os
containers or contribute to their water tightness. recipientes ou a contribuir para a sua
(7.8 – TECH) estanquicidade (7.8 – TECH)
natural cork stopper rolha de cortiça natural
Stopper consisting entirely of natural cork Rolha totalmente constituída por cortiça natural.
Note: Natural cork stoppers that have been Nota: As rolhas naturais que tenham sido
3
submitted to the sealing operation (see 6.5.5) are submetidas à operação de colmatagem (ver 6.5.5)
commonly referred to as colmated natural stoppers. são comummente designadas por rolhas naturais
(5.5 – NORM) colmatadas. (5.5 – NORM)
colmated natural cork stopper rolha de cortiça natural colmatada
The colmated natural cork stopper is a stopper A rolha de cortiça natural colmatada é uma rolha
made of natural cork in which its lenticels are filled feita de cortiça natural em que são obturadas as
4
with a mixture of glues and cork powder from the suas lenticelas com uma mistura de colas e pó de
dimensional finishing processes of natural cork cortiça proveniente dos acabamentos dimensionais
stoppers. (6.1 – REP) das rolhas de cortiça natural. (6.1 – REP)
agglomerated cork stopper rolha de cortiça aglomerada
Stopper obtained by the agglutination of cork Rolha obtida pela aglutinação de granulado de
granules with a size between 0,25 mm and 8 mm, cortiça com dimensão compreendida entre 0,25mm
5
with addition of binders, by means of extrusion or e 8mm, com adição de ligantes, através de extrusão
moulding and composed of at least 51% by weight ou moldagem e composta, pelo menos, por 51 % de
of cork granules. (5.5 – NORM) granulado de cortiça, em peso. (5.5 – NORM)
agglomerated stopper: rolha aglomerada:
6 piece of agglomerated cork, obtained by extrusion peça de cortiça aglomerada, obtida por extrusão ou
or moulding (3.1 – STUD) moldagem (3.1 – STUD)
rolha n+n
n+n stopper
Rolha formada por um corpo de cortiça aglomerada
Stopper formed by a body of agglomerated cork and
e “n” discos de cortiça natural colados num ou em
7 “n” disks of natural cork glued to one or both ends.
ambos os topos.
N.B.: In this designation, “n” indicates the number
Nota: Nesta designação, “n” indica o número de
of disks used. (5.5 – NORM)
discos utilizados. (5.5 – NORM)
technical stopper rolha técnica
Technical stoppers are composed of a very dense As rolhas técnicas são constituídas por um corpo de
body of agglomerated cork with disks of natural cortiça aglomerada, muito denso, com discos de
8 cork glued to one end - or to both ends. Technical cortiça natural colados no seu topo – ou em ambos
stoppers with one disk on each end are called 1+1 os topos. As rolhas técnicas com um disco em cada
technical stoppers; those with two disks of natural topo são designadas rolhas técnicas 1+1. Com dois
cork on each end are called 2+2 technical stopper; discos de cortiça natural em cada topo chamam-se
4
and those with two disks glued at only one of the rolhas técnicas 2+2, e com dois discos em apenas
ends are called 2+0 technical stoppers. (6.1 – REP) um dos topos chamam-se rolhas técnicas 2+0. (6.1 –
REP)
rounded stopper rolha boleada
9 Stopper whose edges of one or two ends were Rolha cujas arestas de um ou dois topos foram
rounded by abrasion. (5.5 – NORM) arredondadas, por abrasão. (5.5 – NORM)
marked stopper
ROLHA MARCADA
10 Stopper whose lateral surface or ends were marked
Rolha cuja superfície lateral ou topos foram
in ink or by fire (7.6 – TECH)
marcados a tinta ou a fogo. (7.6 – TECH)
For this paper, we will consider only one definition, namely [natural cork
stopper] (see line 3 in Table 4), to demonstrate our linguistic and conceptual analysis. However, instead
of using the definitional statement written in Portuguese, we have decided to use its literal translation
into English for clarity.
Error! Reference source not found. 5
Linguistic analysis of the definition of
Concept
Definition in context
stopper consisting entirely of natural cork
Note: Natural cork stoppers that have been submitted to the sealing operation (see 6.5.5) are commonly
referred to as colmated natural stoppers
(Literal translation). Source: (Cork Corpus 5.5 – NORM)
Lexical-semantic
Analysis Lexical marker (LM) Interpretation
relations
stopper [GENERIC]
natural cork stopper
‘is a’ = Ø HYPERNYMY - HYPONYMY natural cork stopper
[is a] stopper
[SPECIFIC]
natural cork stopper ‘consisting entirely natural cork stopper
LINGUISTIC DIMENSION
[consists entirely of] of’ HOLONYMY-MERONYMY [OBJECT]
natural cork natural cork [STUFF]
natural cork stopper sealing operation
[is submitted to] the ‘submitted to’ HOLONYMY-MERONYMY [ACTIVITY]
sealing operation ? = [FEATURE]
natural cork stopper
colmated natural ‘commonly referred
[GENERIC]
stopper [is a] natural to as’ HYPERNYMY - HYPONYMY
colmated natural
cork stopper same as = ‘is a’
stopper [SPECIFIC]
colmated natural results from
sealing operation
stopper [results
HOLONYMY-MERONYMY [ACTIVITY]
from] the sealing = inferred from
colmated = [FEATURE]
operation ‘submitted to’
Table 5 represents the first moment of our study, where we describe the deconstruction of the
definition and present its linguistic analysis. The aim is to analyse the lexical-semantic relations
between terms. The definition of is given in the main sentence, followed by
some encyclopaedic information, namely the note. While the first sentence provides essential
information for understanding what a is made of, the encyclopaedic information
conveys information about what the object is when submitted to a specific operation. The first
information that we obtain from the analysis is that a “is a stopper”. In this
statement, “is a” is a lexical marker that relates term A “natural cork stopper” and term B “stopper”,
giving us a clear hypernym-hyponym relation, where “natural cork stopper” is the hyponym of the
hypernym “stopper”.
5
In the second sentence – inserted as a note in the definition – another piece of information is obtained
from the analysis of the statement “natural cork stoppers that have been submitted to sealing operation”.
Here, the lexical marker is “submitted to” [submetidas à] and relates the term “natural cork stopper”
[rolha natural] to the term “sealing operation” [operação de colmatagem]. The term “sealing operation”
– which indicates an operation/activity – is related by the lexical marker “submitted to” [submetidas à]
to the term “natural cork stopper” – which we already know to be an object. The interpretation of their
meanings allows us to infer that the lexical-semantic relation established is meronymy, subtype
[ACTIVITY-FEATURE] [5] (see Map 1 for the former, and Map 1.1 for the latter, in Figure 3).
Error! Reference source not found.3: Lexical Map 1 and Lexical Map 1.1
4. The conceptual analysis
The conceptual analysis corresponds to the second stage of the analysis of the definition in focus.
The differential characteristics found in this definition are expressed by /natural cork/, /natural/,
6
/colmated/ and /sealing operation/. The observations of this analysis are systematised in Table 6 and are
based on the lexical markers found in the linguistic analysis of the definition. At the same time, based
on the linguistic interpretation of the data, we extrapolated to conceptual relation identifiers.
Error! Reference source not found. 6
The conceptual analysis of the definition of
Aristotelian formula
(X=Y+DC)
X [species] = Y [genus] + DC [differential characteristic]
Conceptual Differential
Conceptual Transcription in
Analysis relation Interpretation characteristi
relation X=Y+DC
identifier cs
stopper natural cork
is_a
natural cork [GENUS] stopper
stopper [is a] SUBSUMPTION natural cork [SPECIES] =
[corresponds to
stopper stopper stopper
LM ‘is a’]
[SPECIES] [GENUS] + DC ?
natural cork
natural cork
has_substance stopper
natural cork stopper
CONCEPTUAL DIMENSION
[SPECIES]
stopper [is [PRODUCT] /natural
[corresponds to ASSOCIATIVE = stopper
made of] natural cork cork/
LM ‘consisting [GENUS]
natural cork [RAW
entirely of’] + natural cork
MATERIAL]
[DC]
has_substance
natural cork natural cork
cork [MATTER]
stopper [is [GENUS] = cork
[corresponds to ASSOCIATIVE natural /natural/
made of] [GENUS] +
LM ‘consisting [PROPERTY]
natural cork natural [DC]
entirely of’]
? [SPECIES] =
natural cork has_process
sealing natural cork
stopper [is
operation = stopper /sealing
submitted [corresponds to ASSOCIATIVE
[PROCESS] [GENUS] + operation/
to] sealing LM ‘submitted
? = [RESULT] sealing
operation to’]
operation [DC]
is_a natural cork colmated
colmated
stopper natural stopper
natural
[corresponds to [GENUS] [SPECIES] =
stopper [is a] SUBSUMPTION /colmated/
the LM colmated natural cork
natural cork
‘commonly natural stopper stopper
stopper
referred as’] [SPECIES] [GENUS]
+ colmated [DC]
As systematised in Table 6, we propose three conceptual relation identifiers, namely, (1)
has_substance, (2) is_a, and (3) has_process.
(1) has_substance is expressed by the lexical marker “consisting entirely of” [totalmente
constituída por], which refers to the substance of the object. As we know from the linguistic analysis,
the term “natural cork” points to the notion of substance, a material that a given object can be made of.
Since is an object made of a substance, we propose the conceptual relation identifier
has_substance to represent such a semantic relation. This semantic relation mirrors a pragmatic
association - e.g., a thematic connection through virtue or experience, or a dependency between
concepts established by the proximity of time and space [3] - in which a is a [PRODUCT]
obtained from a substance, more specifically a [RAW MATERIAL]. From the interpretation of this
information, we assume that an associative conceptual relation is in place, subtype PRODUCT – RAW
MATERIAL, in which stopper points to the meaning of PRODUCT, and natural cork points to the meaning
of RAW MATERIAL. This interpretation can be represented as follows: [stopper] PRODUCT has_substance
[natural cork] RAW MATERIAL.
The dichotomy PRODUCT – RAW MATERIAL has twofold importance at this point of the conceptual
analysis: on the one hand, it underpins the subtype of the associative relation, while on the other hand,
it is included in the Aristotelian formula [8],[6] known as X = Y + DC, where X=specific concept;
Y=genus; and DC=differential characteristics. The purpose of using such a formula is to identify, for
7
the task of concept modelling, the characteristics stated in the definition under analysis. In order to use
such a formula, one must first identify two concepts: the specific concept and its genus. [We will
develop this further in the paper].
(2) is_a relation: is the subordinate concept, which we have labelled
[SPECIES], and is the superordinate concept, which we have labelled [GENUS]. This
assumption can be represented as: [natural cork stopper] SPECIES is_a [ stopper] GENUS. Once the genus
and the species have been identified, we can then insert these two elements in the formula X SPECIES =
Y GENUS + DC, where: X = natural cork stopper; Y = stopper. Differential characteristics are inferred in
a second stage: considering that [stopper] PRODUCT has_substance [natural cork] RAW MATERIAL, we can
conclude that X [natural cork stopper] = Y [stopper] + DC [natural cork]. The first statement of the
definition conveys the information represented by the first interpretation above, with the dichotomy
[SPECIES-GENUS], which can be represented in the form of a conceptual map (see Figure 4). Conceptual
map 1 is built by applying a differentiae dichotomy in which the differential characteristic /natural cork/
underlies one of the subdivision criteria5.
Error! Reference source not found.4: Conceptual Map 1 - two composition types of
in CmapTools
Conceptual Map 1 (Figure 4) is the conceptual representation of the first statement of the definition,
from which we have inferred that a is_a . Two axes of analysis
are considered in this map: Substance and Parts (the ‘Parts’ axis was inferred from Definition 1; see
Table 4). The conceptual information represented here, namely the axes of analysis Substance and Parts
– whose underlying characteristics are /natural cork/, /mono piece/ and /multi piece/ – will be some of
the coordinates for the elaboration of the formal description of the concept NaturalCorkStopper in
5
According to (ISO/FDIS 1087), the “subdivision criterion [is the] type of characteristic according to which a superordinate concept is
divided into subordinated concepts.” (2019 (E), p. 5).
8
Protégé. Finally, will help us to formally describe types of
composed of several Parts – not only made of , but also of
and . Here, the characteristics fall under the axis of analysis
‘Parts’ and are the coordinates for modelling multi-part concepts.
(3) has_process: Following the same method, the analysis of the note from which we obtained
the information: is submitted to /sealing operation/, was represented in a second
map (Figure 5). This piece of information grounds the conceptual relation identifier we have named as
has_process.
Error! Reference source not found.5: Conceptual map of
Conceptual Map 2 (Figure 5) is the representation of the two sentences of the definition in focus.
Therefore, three axes of analysis are now considered: Substance, Parts, and Finishing Processes, to
which the characteristics /with sealing operation/ and /without sealing operation/, were added. As
represented in Conceptual Map 2, the characteristics /with sealing operation/ and /without sealing
operation/ led us to a different level of concept representation, i.e., the concept
, verbally designated by “colmated cork
stopper”, is a specialisation of , in turn, verbally designated by
“natural cork stopper”. Therefore, these two concepts should not be treated at the same level, nor should
they be defined in the same definitional context, either in natural language or in (semi)formal languages.
The conceptual relations we have inferred from the analysis of the lexical markers observed in the
first five definitions (see Table 4), is summarised in Table 7.
Error! Reference source not found. 7
Overview of the conceptual relations inferred from lexical markers
9
Conceptual relation Conceptual A typology of definitional texts governed by
Lexical marker
identifier relation the DC
‘is a’ is_a SUBSUMPTION stopper [SPECIES]= product [GENUS] + [any DC
added to the genus]
‘commonly is_a SUBSUMPTION colmated natural stopper [SPECIES] = natural cork
referred as’ stopper [GENUS] + colmated [DC added to the
genus]
‘is a’ is_a SUBSUMPTION colmated natural cork stopper [SPECIES] = stopper
[GENUS] + [any DC added to the genus]
‘intended to’ has_function ASSOCIATIVE stopper [SPECIES] = product [GENUS] + to seal
bottles [FUNCTION=DC]
‘obtained from’ has_raw_material ASSOCIATIVE stopper [SPECIES] = product [GENUS] + natural cork
[SUBSTANCE=DC]
‘obtained from’ has_raw_material ASSOCIATIVE stopper [SPECIES] = product [GENUS] +
agglomerated cork [SUBSTANCE=DC]
‘obtained from’ has_substance ASSOCIATIVE natural cork [SPECIES] = cork [GENUS] + natural
[SUBSTANCE=DC]
‘obtained from’ has_substance ASSOCIATIVE natural cork [SPECIES] = cork [GENUS] +
agglomerated [SUBSTANCE=DC]
‘intended to’ has_function ASSOCIATIVE stopper [SPECIES] = piece of cork [GENUS] + to seal
containers [FUNCTION=DC]
‘piece of’ has_substance ASSOCIATIVE stopper [SPECIES] = piece [GENUS] + cork
[SUBSTANCE=DC]
‘usually’ has_shape ASSOCIATIVE stopper [SPECIES] = piece of cork [GENUS] +
cylindrical [SHAPE=DC]
‘usually’ has_shape ASSOCIATIVE stopper [SPECIES] = piece of cork [GENUS] + conical
[SHAPE=DC]
‘usually’ has_shape ASSOCIATIVE stopper [SPECIES] = piece of cork [GENUS] +
prismatic quadrangular [SHAPE=DC]
‘sometimes has_process ASSOCIATIVE stopper [SPECIES] = piece of cork [GENUS] +
with’ rounded edges [PROCESS=DC]
‘sometimes has_process ASSOCIATIVE stopper [SPECIES] = piece of cork [GENUS] +
with’ chamfered edges [PROCESS=DC]
‘‘consisting has_substance ASSOCIATIVE natural cork stopper [SPECIES]
entirely of’ = stopper [GENUS]
+ natural cork [SUBSTANCE=DC]
‘consisting has_substance ASSOCIATIVE natural cork [GENUS] = cork [GENUS] + natural
entirely of’ [SUBSTANCE=DC]
‘submitted to’ has_process ASSOCIATIVE ? [SPECIES] = natural cork stopper [GENUS] + sealing
operation [DC]
‘is made of’ has_raw_material ASSOCIATIVE colmated natural cork stopper [SPECIES] = stopper
[GENUS] + natural cork [SUBSTANCE=DC]
‘is made of’ has_substance ASSOCIATIVE colmated natural cork stopper [SPECIES] = natural
cork stopper [GENUS] + colmated [SUBSTANCE=DC]
‘its lenticels are has_process ASSOCIATIVE colmated natural cork stopper [SPECIES] = natural
filled’ cork stopper [GENUS] + filled lenticels
[PROCESS=DC]
‘results from’ has_process ASSOCIATIVE cork powder [SPECIES] =
natural cork [GENUS] + dimensional finishing
process [PROCESS=DC]
‘consisting of’ has_part PARTITIVE stopper [SPECIES] = product [GENUS] + one piece
[PARTS=DC]
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‘obtained from’ has_part PARTITIVE stopper [SPECIES] = product [GENUS] + several
pieces [PARTS=DC]
‘consisting of’ has_part PARTITIVE stopper [SPECIES] = piece of cork [GENUS] + one
element [PARTS=DC]
‘consisting of’ has_part PARTITIVE stopper [SPECIES] = piece of cork [GENUS] + several
elements [PARTS=DC]
As shown in Table 7, differential characteristics (DC) can be any characteristic in a given definition
according to the formula of an intensional definition [4], so that, depending on what is added to the
intension of the [GENUS], the understanding of the concept’s place in the concept system is provided.
The same happens with the associative relation, although with several other axes of analysis involved.
Here, DC share semantic labels in a more productive variety, namely [SUBSTANCE]; [FUNCTION];
[PROCESS] and [SHAPE], given the prolific semantic relations identified between concepts.
5. Building the ontology
For the task of building OntoCork, we used the editor Protégé [7]. OntoCork is an ontology in which
the concepts of the domain of cork are systematised through logical constructs. The descriptive domain
properties (conceptual relations) elaborated to develop the ontology ground on the five axes of analysis
that we have previously retained, namely Part, Substance, Shape, Finishing Process, and Function, as
systematised in Table 8.
Error! Reference source not found. 8
Five core conceptual relations of the ontology
Format in
Axis of analysis Type of conceptual relation
Protégé
FUNCTION hasFunction associative relation, subtype [OBJECT-FUNCTION]
associative relation, covering both subtypes [RAW MATERIAL – PRODUCT] and
SUBSTANCE IsMadeOf
[MATTER/SUBSTANCE – PROPERTY]
PARTS hasStructure partitive relation [PART-WHOLE]
FINISHING
hasProcess associative relation, within the subtype [PROCESS-RESULT]
PROCESS
SHAPE hasShape associative relation, subtype [OBJECT-SHAPE]
For this paper, we will present the description of the characteristics that build up the formal definition
of , a closure with a body-structure of 1 submitted to ,
in addition to the classification provided by the reasoner HermiT 6 as a object (see
Figure 6).
6
HermiT – a plugin reasoner of Protégé (http://www.hermit-reasoner.com/)
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Error! Reference source not found.6: Concept description of
ColmatedMonoPieceNaturalCorkStopper, in Protégé
Figure 7 is the ontological representation of ColmatedMonoPieceNaturalCorkStopper, in
Ontograf7, where we can observe several concepts systematised, either vertically: in a hierarchical
dependency, or horizontally: in a pragmatic (associative) dependency, according to the differential
characteristics. For clarity, we have decided to elide the visualisation of the associative relations
between concepts that are not in focus in the following lines.
Error! Reference source not found.7: Ontological representation of
ColmatedMonoPieceNaturalCorkStopper, in Ontograf
As illustrated in Figure 7, the ColmatedMonoPieceNaturalCorkStopper is a specification of
MonoPieceNaturalCorkStopperWithFinishingProcess. The subsumption relation is represented by
vertical blue arcs, and the associative relations are represented by horizontal dashed lines. The concepts
7
https://protegewiki.stanford.edu/wiki/OntoGraf
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ColmatedMonoPieceNaturalCorkStopper and LenticelsColmation are linked by the associative
relation, subtype [PROCESS-RESULT]: hasLenticelsColmationOperation. This conceptual relation is based on
the differential characteristic /with sealing operation/, which was drawn from the analysis of the
definition of . Thus, hasLenticelsColmationOperation is the associative relation that
induces the specification of MonoPieceNaturalCorkStopperWithFinishingProcess by differentia.
Finally, it is also possible to see a hierarchical representation of FinishingProcesses, in which the
involved operation of the concept we have just described is assigned as the most specific concept of
this hierarchy. The interpretation of this subsumption is: LenticelsColmation is a kind of
QualityTreatment, which is a kind of SurfaceTreatment, which in turn is a kind of Semi-
finishingProcess, all of these are kinds of FinishingProcesses.
6. Conclusion
With this research, we wanted to explain the method used to build an ontology from human analyses
of linguistic data. Linguistic and conceptual levels of analysis are to be analysed in relation to one
another but as distinct phenomena. Texts are vehicles for knowledge transfer. Analysing texts to extract
the characteristics of concepts, linguistically expressed by lexical markers pointing to lexical-semantic
relations, allowed us to effectively capture the conceptual relations that are specific to the domain
through the formula X=Y+DC. As demonstrated in this study, we were able to propose a preliminary
conceptual organisation of the subject field. We have bridged three main aspects in our study: (i) the
classical aspects of the Aristotelian logic; (ii) the methodology of our terminological work – where
characteristics play a fundamental role in the analysis or the drafting of intensional definitions; and (iii)
the formal definitions, for which we have used Protégé and the inherent Web Ontology Language
(OWL) [12] to formally describe the concepts of the domain in order to relate them via abstract syntaxes
and thus achieve formal reasoning, as concepts are consistently defined in a ‘reason-able’ ontology.
In future work, we intend to model the conceptual and the linguistic information contained in the
resources we have developed, namely the ontology, the corpus, and a glossary (in progress) developed
with Lexonomy8, as linked data with the use of interoperable Linked Open Vocabularies9.
7. References
[1] Atkins, S., Clear, J., & Ostler, N. (1992). Corpus Design Criteria. Literary and Linguistic
Computing, 7(1), 1 - 16. doi:10.1093/llc/7.1.1
[2] Baker, P., Hardie, A., & McEnery, T. (2006). A Glossary of Corpus Linguistics. Edinburgh:
Edinburgh University Press.
[3] ISO 704. (2009). Travail terminologique - Principes et méthodes. NF ISO 704, 1er tirage 2009-
12-P. La Plaine Saint-Denis: Association Française de Normalisation.
[4] ISO/FDIS 1087. (2019 (E)). Terminology work and terminology science - Vocabulary. Suisse:
ISO.
[5] L'Homme, M. C. (2004). La Terminologie: principes et techniques - Paramètres. Montréal,
Canadá: Les presses de l'Université de Montréal.
[6] Meyer, I. (2001). Extracting Knowledge-Rich contexts for terminography: a conceptual and
methodological framework. In D. Bourigault, C. Jacquemin, & M.-C. L'Homme (Eds.), Recent
Advances in Computational Terminology (Vol. 2, pp. 279 - 302). Amsterdam / Philadelphia:
John Benjamins B.V.
[7] Musen, M. A. (2015). The Protégé project: A look back and a look forward.
doi:10.1145/2557001.25757003
[8] Pearson, J. (1998). Terms in context. Amsterdam: John Benjamins B.V.
[9] Pottier, B. (1992). Théorie et analyse en Linguitique (2, corrigée ed.). Paris: HACHETTE,
Supérieur.
8
https://github.com/elexis-eu/lexonomy
9
https://lov.linkeddata.es/dataset/lov/
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[10]Ramos, M. (2020). Knowledge Organization and Terminology: application to Cork. Lisboa:
NOVA FCHS; LISTIC - Université Savoie Mont Blanc. Obtido de
http://hdl.handle.net/10362/111722
[11]Ramos, M. (2020). OntoCork. NOVA FCSH. doi:https://doi.org/10.34619/a27q-1ryd
[12]W3C. (2004). OWL Web Ontology Language Reference. (M. Dean, & G. Schreiber, Eds.)
Retrieved from W3C Recommendation 10 February 2004: https://www.w3.org/TR/owl-ref/
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