Elena Cardillo, Andrei Tamilin and Luciano Serafini
FBK-IRST, via Sommarive 18, 38100 Povo (TN) , Italy
Keywords: Medical Terminology, Medical Vocabulary Acquisition, Knowledge Acquisition, Consumer Healthcare.
Abstract: In spite of the improvements in Healthcare Informatics in answering consumer needs, it is still difficult for
laypersons who do not have a good level of healthcare literacy, to find, understand, and act on health
information. This is due to the communication gap which still persists between specialized medical
terminology used by healthcare professionals and “lay” medical terminology used by healthcare consumers.
So there is a need to create consumer-friendly terminologies reflecting the different ways consumers and
patients express and think about health topics. An additional need is to map these terminologies with
existing clinically-oriented terminologies. Following this direction, this work suggests a hybrid
methodology to acquire consumer health terminology for creating a Consumer-oriented Medical Vocabulary
for Italian that mitigates this gap. This resource could be used in Personal Health Records to provide
translation, search, and classification services, helping users to improve access to their healthcare data. In
order to evaluate this methodology we mapped “lay” terms with standard specialized terminologies to find
overlaps. Results showed that our methodology of acquisition provided many “lay” terms that can be
considered good synonyms for medical concepts.
With the advent of the Social Web and Healthcare
Informatics technologies, we can recognize that a
linguistic and semantic discrepancy still exists
between specialized medical terminology used by
healthcare providers or professionals, and the so
called “lay” medical terminology used by healthcare
consumers. The medical communication gap became
more evident when consumers started to play an
active role in healthcare information access. In fact
they have become more responsible for their
personal healthcare, exploring information sources
on their own, consulting decision-support healthcare
sites on the web, and using patient-oriented
healthcare systems, which allow them to directly
read and interpret clinical notes or test results and to
fill in their Personal Health Record. During this
disintermediated interaction consumers can use only
their own knowledge, experience and preferences,
and this can often generate a wrong inference of the
meaning of a term, or the mis-association of a term
with its context (Zeng and Tse, 2006).
To help consumers fill this gap, the challenge is
to sort out the different ways consumers
communicate within distinct discourse groups and
map the common, shared expressions and contexts
to the more constrained, specialized language of
healthcare professionals. Though much effort has
been spent on the creation of medical resources such
as terminologies or classification systems, used
above all to help physicians in filling in Electronic
Health Records, there is little work based on the use
of consumer-oriented medical terminology, and in
addition most existing studies have been done only
for English.
A consumer-oriented medical terminology can
be defined as a “collection of forms used in health-
oriented communication for a particular task or
need by a substantial percentage of consumers from
a specific discourse group and the relationship of
the forms to professional concepts” (Zeng and Tse,
2006). Such terminology can be mainly used for
three bridging roles between consumers and health
applications: a) Information Retrieval, to facilitate
automated mapping of consumer-entered queries to
technical terms, producing better search results; b)
Cardillo E., Tamilin A. and Serafini L. (2009).
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 64-71
DOI: 10.5220/0002303900640071
Translation of Medical Records, supplementing
medical jargon terms with consumers-
understandable names to help patients interpretation;
c) Health Care Applications, to help integrating
different medical terminologies providing automated
mapping of consumer expressions to technical
concepts (e.g. querying for “short of breath” and
receiving information also for the concepts
Given this scenario, the present work proposes a
hybrid methodology for the acquisition of consumer-
oriented medical knowledge and “lay” terminology
expressing particular medical concepts, such as
symptoms and diseases, for consequent creation of a
Consumer-oriented Medical Vocabulary for Italian.
We are particularly interested in performing analysis
of the clinical mapping between this consumer-
oriented terminology and the more technical one
used in standards Medical Classification Systems or
Nomenclatures. This resource could be integrated
with existing lexical and semantic medical
resources, and used in healthcare systems, like
Personal Health Records, to help consumers during
the process of querying and accessing healthcare
information, so as to bridge the communication gap.
2.1 Consumer-oriented Medical
Over the last two decades research on Medical
Terminologies has become a popular topic and the
standardization efforts have established a number of
terminologies and classification systems such as
UMLS Metathesaurus
, SNOMED International
ICD-10 (International Classification of Diseases)
and ICPC-2 (International Classification of Primary
, as well as conversion mappings between
them to help medical professionals in managing and
codifying their patients health care data. They
concern, in fact, “the meaning, expression, and use
of concepts in statements in the medical records or
other clinical information systems” (Rector, 1999).
Despite the wide use of these terminologies, as we
have already mentioned, the vocabulary problem
continues to plague not only health professionals and
their information systems, but also consumers and in
particular laypersons, who are the most damaged by
the increased communication gap.
To respond consumer needs to support personal
healthcare decision-making, during the last few
years, many researchers have labored over the
creation of lexical resources that reflect the way
consumers/patients express and think about health
topics. One of the largest initiatives in this direction
is the Consumer Health Vocabulary Initiative
, by Q.
Zeng and colleagues at Harvard Medical School,
resulted in the creation of the Open Access
Collaborative Consumer Health Vocabulary (OAC
CHV) for English. It includes lay medical terms and
synonyms connected to their corresponding
technical concepts in the UMLS Metathesaurus.
They combined corpus-based text analysis with a
human review approach, including the identification
of consumer forms for “standard” health-related
concepts. Also (Soergel et al., 2004) tried to create
such a vocabulary to identify consumer medical
terms and expressions used by lay people and health
mediators. In fact they associated a Mediator
Medical Vocabulary with the consumer-oriented
one, and mapped them to a Professional Medical
Vocabulary. Even in this case the standard
terminology used for mapping was UMLS.
These and other similar studies examined large
numbers of consumer utterances (i.e., hundreds of
thousands of tokens) and consistently found that
between 20% and 50% of consumer health
expressions were not represented by professional
health vocabularies. Furthermore, a subset of these
unrepresented expressions underwent human
review. In most of these cases they performed
automatic term extraction from written texts, such as
healthcare consumer queries on medical web sites,
postings and medical publications. An overview of
all these studies can be found in (Keselman et al.,
It is important to stress that there are only few
examples of the applications as far as these initiative
are concerned. For example, in (Kim et al., 2007)
we find an attempt to face syntactic and semantic
issues in the effort to improve PHR readability,
using the CHV to map content in Electronic Health
Records (EHR) and PHR; (Zeng et al., 2007)
designed and implemented a prototype text
translator to make EHR and PHR more
comprehensible to consumers and patients. On the
other hand (Rosembloom et al., 2007) developed a
clinical interface terminology, a systematic
collection of healthcare-related phrases to support
clinicians' entries of patient-related information into
computer programs such as clinical “note capture”
and decision support tools, facilitating display of
computer-stored patient information to clinician-
users as simple human-readable texts.
Concerning multilingual consumer-oriented
health vocabularies, we can mention the initiative of
the European Commission Multilingual Glossary of
Popular and Technical Medical Terms
, in nine
European languages, but it is a limited medical
vocabulary for medicinal product package inserts
accessible to consumers. In fact, it consists of a list
of 1,400 technical terms frequently encountered in
inserts, with corresponding consumer terms in all the
languages of the European Community. Greater
overlap between technical and lay terms was
observed for Romance languages and Greek than for
Germanic languages (except English), and some
technical terms had no lay equivalent.
2.2 Knowledge Acquisition in
Knowledge Acquisition process aims at identifying
and capturing knowledge assets and terminology to
populate a knowledge repository for a specific
domain. Central areas of this task are: terminology
work, relevant for the special subject field, including
terminography; content analysis of documents;
extraction of knowledge from various sources. A
major part of Knowledge Acquisition is capturing
knowledge from experts, a task that is made cost-
effective and efficient by using knowledge models
and special elicitation techniques. These techniques
should be used in different phases of the process,
since each of them permits the capture of a specific
typology of knowledge and the achievement of
specific aims. The most common techniques are
Interviews, direct observation of expert
performances to extract procedural knowledge,
mostly connected to manual skills, such as Think
Aloud Problem Solving, Self-report, and Shadowing.
Other techniques, such as Card Sorting, Repertory
Grid, and Twenty Questions, are useful for
understanding how experts conceptualise knowledge
related to their own domain of reference (Milton,
In a task of Knowledge Acquisition, it is
important to identify two main components:
knowledge types, referred to knowledge orientation
and domain; and modalities, referred to the
representation medium in which knowledge exists.
In Knowledge Acquisition for the Healthcare
domain, according to (Abidi, 2008), many different
types of knowledge, which directly contribute to
clinical decision-making and care planning, can be
identified: Patient, Practitioner, Medical, Resource,
Process, Organizational, Relationship and, finally,
Measurement Knowledge. In the present work we
will only deal with Medical Knowledge and Patient
Knowledge. These knowledge types are represented
by different knowledge modalities. The most
common ones are: Tacit Knowledge of practitioner,
Explicit Knowledge, Clinical experiences,
Collaborative Problem-solving discussions, Social
Knowledge, etc. In our work we focus on Explicit
Knowledge, Clinical Experience and Social
Knowledge. In particular the last modality can be
viewed in terms of a community of practice and their
communication patterns, interest and expertise of
individual members.
In this study we focused on the acquisition of
consumer-oriented knowledge about a specific
subset of healthcare domain that includes Anatomy,
Symptomatology and Pathology. For that task we
have chosen to use a hybrid methodology for the
identification of “lay” terms, words, and expressions
used by Italian speakers to indicate
Symptoms/Signs, Diseases and Anatomical
Concepts. Three different target groups were
considered for the application of our approach: First
Aid patients subjected to a Triage Process; a
community of Researchers and PhD students with a
good level of healthcare literacy, and finally a group
of elderly people with a low level of healthcare
literacy. The proposed methodology consists of the
following steps:
1. Familiarization with the domain and
exploitation of existing common lexical
resources (Glossaries, Thesauri, Medical
Encyclopedias, etc);
2. Choice and application of three different
Elicitation Techniques to the mentioned
groups of people:
a. Collaborative Wiki-based Acquisition;
b. Nurse-assisted Acquisition;
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
c. Interactive Acquisition combining
traditional elicitation techniques (Focus
Groups, Concepts Sorting and Games);
3. Automatic Term Extraction and analysis of
acquired knowledge by means of a Text
Processing tool;
4. Clinical review of extracted terms and manual
mapping to a standard medical terminology,
performed by physicians;
5. Evaluation of results in order to find candidate
terms to be included in the Consumer-oriented
Medical Vocabulary.
3.1 Wiki-based Acquisition
The first method for acquiring consumer-oriented
medical knowledge is based on the use of a
Semantic Media Wiki system
, an easy to use
collaborative tool, allowing users to create and link,
in a structured and collaborative manner, wiki pages
on a certain domain of knowledge. Using our online
system, users created wiki pages for
describing symptoms and diseases, using “lay”
terminology, specifying in particular the
corresponding anatomical categorization, the
definition and possible synonyms.
The system has been evaluated over a sample of
32 people: researchers, PhD students and
administrative staff of our research institute (18
females, 14 males, between 25 and 56 years old). In
one month, we collected 225 wiki pages, 106 for
symptoms and 119 for diseases, and a total of 139
synonyms for the inserted terms. It was very
interesting to test here also the understanding of the
collaborative nature of the Wiki for the specific task,
which gave users the possibility not only to insert
medical terms by creating wiki pages, but also to
update, cancel or correct the inserted information,
and above all to modify wiki pages added by other
users, in order to reach a convergence on the
common sense of medical terminology. In our case,
users were reluctant to modify concepts added by
others, even in the case of evident mistakes in
definitions or categorization (only 7 people out of 32
provided changes to wiki pages). Some examples of
categorization mistakes that had not been modified
are “Singhiozzo” (Hiccup), and “Mal di Testa”
(Headache), both categorized as Diseases instead of
Symptoms. In some cases, when users were in doubt
about the right categorization of a concept, they
inserted it in both the categories, e.g., “Ustione”
(Burning). This test highlighted the fact that users
had problems in categorizing medical terms - mainly
due to their clinic ambiguity - and also the erroneous
use of these terms by them daily.
3.2 Nurses-assisted Acquisition
The second acquisition technique involved the
nurses of a First Aid Unit (in a Hospital of the
Province of Trento)
, as figures of mediation for the
acquisition of terminology about patient symptoms
and complaints. Nurses here help patients to express
their problems using the classical subjective
examination performed during the Triage Process.
The Triage activity has the aim of prioritizing
patients according to the severity of their condition,
on the basis of examination costing few minutes.
This acquisition method involved 10 nurses, around
60 patients per day and a total of 2.000 Triage
Records registered in one month. During this period
nurses acquired the principal problems (symptoms
and complaint) expressed by their patients using
“lay” terminology and inserted them in the Triage
Record together with the corresponding medical
concepts usually used for codifying patient data. For
example, the lay expression “Ho i crampi alla
pancia” (I have a stomach ache) was inserted in the
Triage Record together with the corrisponding
medical concept “Addominalgia” (Abdominal pain).
3.3 Focus-group Acquisition
The last method used in our study consisted in
merging three different traditional elicitation
techniques: Focus Group, Concepts Sorting, and the
more experimental Board Games, in order to allow
interaction and sharing situations to improve the
process of acquisition. We have applied this
techniques to a community of 32 elderly persons in a
Seniors Club in the Province of Trento, between 65
and 83 years old.
During the process of acquisition we divided
participants into 4 groups, of 8 people each,
assigning to each one a specific body part category
(for instance, head and neck, abdomen and back,
arms, hands and chest, pelvic area, legs and feet).
Each group was asked to write on little cards all
known symptoms and diseases related to the
assigned area, starting from personal experience, and
comparing their idea with other members of the
Medicina d’Urgenza e Pronto Soccorso del Presidio Ospedaliero
di Cles (Trento): http://www.apss.tn.it/Public/ddw.aspx?n=26808
group to find a common definition for the written
terms. Time allowed for card writing was about 20
minutes. About 160 medical terms were collected.
Then, all these terms were analyzed together,
creating discussions, exchanging opinions on term
definitions, synonyms, and recording preferences
and common sense. At the end of the discussion
about each medical concept, all participants gave
preferences for choosing the right body system
categorization of that concept. For this
categorization we provided a panel with 14 different
problem areas and body systems (digestive,
neurological, musculoskeletal, lymphatic, endocrine,
etc.). This allowed us not only to collect lay
terminology used by elderly people, but also to
understand how they define and categorize medical
concepts, in order to compare these results with
these obtained with the other two techniques
mentioned above.
Three sets of collected data, including the
transcription of the Focus Group activity with
elderly persons, were further processed and
analyzed, to detect candidate consumer-oriented
terms, with the tool Text-2-Knowledge (T2K),
developed at the Institute of Computational
Linguistics of Pisa
. This tool allowed us to
automatically extract terminology from the data sets
and to perform typical text processing techniques
(normalization, pos tagging, chunking, etc.),
calculating, in addition, statistics such as term
frequency on the extracted data. The computational
system adopted by the tool includes a specific plug-
in for the analysis of Italian. It provides, as final
output, a term-based vocabulary whose added value
is represented by the terms’ semantic and conceptual
information regarding the vocabulary itself. These
terms, which can be either single or multi-word
terms, are organized in a hierarchical
hyponym/hyperonym relation depending on the
internal linguistic structure of the terms (Bartolini et
al., 2005); that is, by sharing the same lexical head.
In spite of the advantages of the automatic
extraction process, allowing for extraction of many
compound terms, a good amount of terms, certainly
representative of consumer medical terminology,
were not automatically extracted, since, due to the
quantitative limits of the corpus dimensions, their
occurrence was inferior with respect to the
predefined threshold value. Consequently, we
performed an additional manual extraction to take
into account such rare terms, usually mentioned by a
single participant. Statistical results about the three
different data sets are further discussed in Section 6.
Term extracted by T2K were reviewed by two
physicians to find errors and incongruities in
categorization and synonymy. Many mistakes were
found in the first set (Wiki-based), where a wrong
categorization was assigned to 25 terms, and were
wrong synonyms were expressed for 8 terms. Many
mistakes were also found in the third set (Elderly
people), where wrong categorization were assigned
to 40 terms, e.g., “Giramento di Testa” or
“Vertigini” (Vertigo or Dizzines), in the
Cardiovascular System instead of the right
Neurological one. Concerning the second data set,
clinical review was performed during the process of
Triage by a nurse and a physician .
During the second part of our clinical review,
physicians have been asked to map a term/medical
concept pair by using a professional medical
terminology - in this study the International
Classification for Primary Care 2
Edition (ICPC2).
ICPC2 addresses fundamental parts of healthcare
process: it is used in particular by general
practitioners for encoding symptoms and diagnosis.
It has a biaxial structure that consider medical
concepts related to Symptoms, Diseases and
Diagnoses, and Medical Procedure, according to 17
Problem Areas/Body Systems. In previous work we
encode ICPC-2-E using a recently developed Web
Ontology Language (OWL)
(both for English and
Italian), that also provides the formalization of the
existing clinical mapping with the ICD10
classification system, as shown in (Cardillo et. al.,
2008). By means of this mapping we want to
reconstruct the meaning (concept)
inherent in the lay
usage of a term, and then to agree that
between lay and professional terms exists on the
basis of this deeper meaning, rather than the lexical
form. Five different types of relations are possible
between consumer terms and ICPC2 medical
Exact Mapping between the pairs; this
occurs when the term used by a lay person
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
can be found in ICPC2 terminology and
both terms correspond to the same concept.
E.g., the lay term “Febbre” (Fever) would
map to the ICPC2 term “Febbre”, and both
will be rooted to the same concept.
Related Mapping; it involves lay synonyms
and occurs when the lay term does not exist
in the professional vocabulary, but
corresponds to a professional term that
denotes the same (or closely related)
concept. E.g., lay term “Sangue dal Naso”
(Nosebleed) corresponds to “Epistassi”
Hyponymy Relation; this occurs when a lay
term can be considered as term of inclusion
of a ICPC2 concept. E.g., lay term
“Abbassamento della Voce” (Absence of
Voice) is included in the more general
ICPC2 concept “Sintomo o disturbo della
voce” (Voice Symptom/Complaint).
Hyperonymy Relation; in this case the lay
term is more general than one or more
ICPC2 concepts, so it can be considered as
its/their hyperonym. E.g., the term
“Bronchite” (Bronchitis) is broader than
“Bronchite Acuta/ Bronchiolite” (Acute
Bronchitis/ Bronchiolitis) e “Bronchite
Cronica” (Chronic Bronchitis) ICPC2
Not Mapped; it comprises those lay terms
that cannot be mapped to the professional
vocabulary. These can be legitimate health
terms whose omission reflects real gaps in
existing professional vocabularies; or they
can represent unique concepts reflecting lay
models of health and disease. E.g., the lay
term “Mal di mare” (Seasickness).
As previously mentioned, our methodology of
acquisition allowed us to acquire varied consumer-
terminology and to perform an interesting
terminological and conceptual analysis. Tables 1-4
provide term extraction and mapping evaluation in
terms of a statistical analysis. By means of the term
extraction process, we were able to extract a total of
692 medical terms from 225 Wiki pages, 375 of
which were not considered pertinent to our aim. We
performed mapping analysis on 587 terms as
summarized in Table 1.
Table 1: Wiki term collection.
Symptoms 306 26 50 40 9
Diseases 140 42 19 38 38
Anatomy 141 105 11 16 4
Other 375 / / / /
Tot. 962
Not Mapped. 186 Not Considered:375
We can observe that most of the exact mappings
with ICPC2 are related to anatomical concepts, and
which many synonyms in lay terminologies and
inclusion terms were found for symptoms. Table 2
shows the results related to the Triage acquisition
Table 2: Nurses-assisted term collection.
Symptoms 508 134 197 177
Diseases 325 86 94 145
Anatomy 275 120 95 60
Other 1281 / / /
Tot. 2389 Not Considered: 1281
We extracted a total of 2389 terms from 2.000
Triage records, but about half of these terms were
considered irrelevant for our evaluation, so mapping
was provided only for 1108 terms. Contrary to the
previous results, here is interesting to highlight the
high presence of lay terms used for expressing
symptoms with exact mappings to ICPC2, but also
many synonyms in lay terminology for ICPC2
symptoms and diseases. This is particularly related
to the context chosen for the acquisition, where
patients just ask for help about suspected symptoms
and complaints. Table 3 shows the results related to
the data acquisition from Elderly people.
Table 3: Focus Group /Game with Elderly Persons.
Symptoms 79 35 44 /
Diseases 87 29 54 4
Anatomy 77 51 18 8
Other 68 / / /
Tot. 321 Not Considered: 78
Concerning the last data set, 321 medical terms
were extracted by the transcription of the Focus
Group/Game activity. Here is interesting to note that
all the symptoms extracted had corresponding
medical concept in ICPC2 terminology.
Table 4 compares the three data set together and
shows that the most profitable methods for acquiring
consumer-oriented medical terminology was the one
assisted by nurses. But the limit of this method is
that it is time-consuming for nurses who have to
report all the patient “lay” health expressions. While
Wiki-based method, even if not exploited for the
collaborative feature, has demonstrated good
qualitative and quantitative results. Furthermore, are
interesting the results concerning mapping to ICPC2,
because 2/3 of the lay terms collected are covered by
ICPC2 terminology.
Table 4: Results Overview.
eHealthWiki 962 398 186
Nurse-assisted 2389 726 382
Focus Groups 321 231 12
Tot. 3662 1355 580
To conclude our evaluation we have to highlight
that comparing the three sets of collected terms, the
overlap is only of 60 relevant consumer medical
terms. The total overlap with ICPC2 is about 508
medical concepts on a total of 706 ICPC2 concepts.
This means that all the other mapped terms can be
considered synonyms or quasi synonyms of the
ICPC2 concepts. The large number of not mapped
terms and the low overlap between the three sets of
extracted terms demonstrate that we collected a very
variegated range of medical terms, many compound
terms and expressions. These terms can be
representative of the corresponding technical terms
present in standard medical terminologies, and can
be used as candidate terms for the construction of
our Consumer-oriented Medical Vocabulary for
In this paper we have presented a hybrid
methodology for acquiring consumer-oriented
medical Knowledge and Terminology for Italian,
consisting of lay expressions and terms used to
indicate Symptoms, Diseases and Anatomical
Concepts. We applied three explorative elicitation
techniques to three different groups of people, and
we compared results on the basis of a term
extraction process, for statistical analyses, and on a
clinical mapping procedure, for finding overlaps
between extracted lay terms and specialized medical
concepts in the ICPC2 medical terminology. Our
methodology showed encouraging results, because it
allowed us to acquire many consumer-oriented
terms, to find a low overlap with medical concepts
and a high number of related mappings (most of the
time synonyms) to the ICPC2 terminology.
Taking each of these acquisition techniques
alone, they do not allow a good coverage of the
whole domain of pathology and symptomatology.
In the first case, in fact, most of the terms provided
by Researchers and PhD students are related to
Digestive and Musculoskeletal Systems, and Skin,
while in the case of the Triage activity patients
expressed most of the time symptoms related to
Musculoskeletal System (due to the geographic
context and period of the acquisition, i.e. mountain–
skiing area, end of winter), Respiratory System, and
Cardiovascular System. Furthermore, also in the
third acquisition, terms are mostly related to the
these Body Systems. Using a hybrid approach in
merging these techniques and involving a more
varied sample of people would improve the results,
both from the qualitative and the quantitative point
of view. Another limit could be seen in the process
of manual mapping performed by physicians. After
this pilot study we plan to implement a semi-
automatic procedure for mapping lay and specialized
terminology, which will be associated to the process
of automatic term extraction, and validated by the
review of physicians.
In the approaches described in Section 2,
consumer-oriented health vocabularies were
developed by working only on big written corpora
(forum postings and queries to medical websites),
using machine learning algorithm and statistical
methods (naive Bayesian classifiers, C-value,
Logistic Regression etc.) to extract consumer-
oriented terminology. In comparison to these
approaches we gave more importance to qualitative
data, focusing on different methods for acquiring not
merely lay terminology but also knowledge directly
from consumers in different scenarios related to
General Practice. This allowed also to try to
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
understand how consumers make use of medical
terminology, how common expressions daily used in
health communication really match onto medical
concepts used by professionals.
To improve the results of the Knowledge
Acquisition process and to extract more variegated
consumer terminology, not related to the regional
context, one of the future tasks is to perform a
Knowledge Acquisition Process involving people in
a Social Network. This would allow to extend our
sample, including younger people. This task would
be very interesting for comparing results with what
resulted from the previous methodologies. Another
important improvement would be the analysis of
written texts such as forum postings of an Italian
medical website for asking questions to on-line
. Data extracted in this way could also be
used to validate the acquired verbal terminology, by
providing preferences between terms according to
frequency and familiarity score.
We would like to thank Antonio Maini and Maria
Taverniti, who provided us with useful support
respectively in the process of Knowledge
Acquisition and Term Extraction.
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