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As real-life application scenario of our SCMS
platform, we report a system customization useful
for managing the semantic matching between the re-
quired professional profiles by a Public Administra-
tion (PA) and the available skills in a set of curricula
vitae with respect to a given call in the ICT area.
More in details, the PA employees need to verify
the correct matching between the professional profiles
and the skills reported in the curricula that partici-
pants have submitted for a public tender, with respect
to the required profiles: this facility has to help the
scoring process of competitors for the tender.
The first step consists of the system knowledge
base building that has to represent and model the typi-
cal skills and professional profiles in the ICT context.
To this aim, we created the knowledge base start-
ing from the development of a thesaurus of profes-
sional profiles - we use the EUCIP (EUCIP) classifi-
cation - then enriched with the skills reported within
the DISCO II (DISCO II) available thesaurus.
EUCIP ( European Certification of Informatics
Professionals) is the European standard for describ-
ing skills of ICT professionals.
DISCO, the European Dictionary of Skills and
Competences, is an online thesaurus that currently
covers more than 104,000 skills and competence
terms and approximately 36,000 example phrases.
Available in eleven European languages, DISCO is
one of the largest collections of its kind in the edu-
cation and labour market.
The DISCO Thesaurus offers a multilingual and
peer-reviewed terminology for the classification, de-
scription and translation of skills and competences. It
is compatible with European tools such as Europass,
ESCO, EQF, and ECVET, and supports the interna-
tional comparability of skills and competences in ap-
plications such as personal CVs and e-portfolios, job
advertisements and matching, and qualification and
learning outcome descriptions.
The construction of the knowledge base has been
realized by defining a new ontology and a new the-
saurus that considers the EUCIP ICT professional
profiles and enriches them with the skills present in
the DISCO II thesaurus, defining at the same time
proper relations among such entities.
For this purpose, we have been supported by a do-
main expert in order to establish the right relation-
ships between skills and profiles, and to validate them.
In the following, we describe the necessary steps to
accomplish the annotation process of resumes.
1. Resumes submitted by contractors are loaded into
the SCMS platform through the User Interface,
and in particular, exploiting the described Content
Editing and Semantic Lifting facilities.
2. The Semantic Engine semantically enriches each
received content: it analyzes the text and, through
the execution of the NLP pipeline, provides the
Entity Annotation process. Through the Linking
process, extracted entities are then linked to well-
known entities of the reference domain (that in
this scenario are properly represented by profes-
sional skills). The obtained semantic information
is finally then stored, together with the related re-
sume, and indexed for the Semantic Search pur-
poses.
3. The User Interface shows the results of the Se-
mantic Lifting obtained through the Annotation
process application, highlighting the words that
cover a certain skill and showing the related pro-
fessional profiles.
In order to provide a set of facilities for resumes’
validation, the SCMS has been equipped with a func-
tionality that allow users to check if the skills and the
professional profiles match with those ones required
by the tender.
The User Interface (see Fig. 5) shows how the
user can easily retrieve the correspondence between
the skills resumes and the professional profiles. In
particular, in the same view, it is possible to show the
required skills together with those ones present in the
resume, but not necessarily desired.
Figure 5: Resume Analysis.
The professional profile and skills - that the Se-
mantic Engine has inferred - are then compared with
the required ones showing the percentage amount of
matching, calculated as a confidence parameter (see
Fig. 6).
This simple business scenario, regarding e-
government applications, can also be applied to other
cases, concerning the composition of a work team at
the start of new incoming projects in an ICT company,
for example.
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