=Paper=
{{Paper
|id=Vol-2139/226-235
|storemode=property
|title=Use of semantic technologies in the process of recognizing the outcomes of non-formal and informal learning
|pdfUrl=https://ceur-ws.org/Vol-2139/226-235.pdf
|volume=Vol-2139
|authors=Serhii Pryima,Julia Rogushina,Oksana Strokan
|dblpUrl=https://dblp.org/rec/conf/ukrprog/PryimaRS18
}}
==Use of semantic technologies in the process of recognizing the outcomes of non-formal and informal learning==
https://ceur-ws.org/Vol-2139/226-235.pdf
UDC 004.853, 004.55 UkrESCO can be considered as an intelligent superstructure over existing systems that compares and evaluates competencies, matches
qualifications with vacancies and form a passport of acquired competencies. Practical implementation of UkrESCO becomes an effective
tool for the formation in Ukrainian society of understanding of the value of throughout life education in the personal and professional self-
development of person.
Key words: labour market, market of educational services, occupation, knowledge, skill, ontology, Semantic Web, ESCO.
USE OF SEMANTIC TECHNOLOGIES IN THE PROCESS OF The problem of recognizing the outcomes of non-formal and informal learning
The socioeconomic challenges of Ukraine exacerbate the need for the effective use of the country's human
RECOGNIZING THE OUTCOMES OF NON-FORMAL AND potential. Aging of the population, the negative balance of interstate migration, imbalances in the structure of demand
INFORMAL LEARNING and supply in the labour market require innovative approaches to address the problem of the country's population
employment and, as a result, the productivity and competitiveness of the national economy.
S.М. Pryima, J.V. Rogushina, О.V. Strokan' A prerequisite for the effective use of human potential through the individual's approaching the new possibilities
in the labour market is the recognition of the outcomes (knowledge, skills, abilities and competences) of non-formal and
informal learning. Such recognition enables a better combination of skills and abilities and, as a consequence, facilitates
В роботі проаналізовано публікації, пов’язані з тенденціями розвитку національних систем кваліфікацій, які мають пов’язати ринок professional and geographical mobility, satisfies the lack of skills and abilities in the growing sectors, accelerates
освітніх послуг з ринком праці. Такий аналіз дозволяє визначити, що ефективним інструментом для рішення цієї проблеми є ESCO
– Багатомовний класифікатор європейських навичок, компетенцій, кваліфікації та професій. ESCO визначає і класифікує навички
economic renovation.
(як «м’які», так і «жорсткі»), компетенції, кваліфікації і професії, які мають значення для європейського ринку праці, освіти та The opposition to rapid economic and technological changes, frequent workplace changes throughout life
професійної підготовки. Класифікатор ESCO є основою для створення «паспорту набутих компетенцій» і використовується у prompts the person to become more and more involved in the learning process, to master the most demanding skills
кількох ініціативах Європейської Комісії у сфері навичок та кваліфікацій, спрямованих на підвищення прозорості ринку праці та more energetically in order to increase chances in the labour market, and to improve one's own well-being.
освітніх систем. Розробка інструментальних засобів, які дозволять ефективно використовувати ESCO та забезпечують перехід від
кваліфікаційної моделі до повноцінних компетентнісних моделей, вбачається актуальним і своєчасним науковим завданням
In today's globalized world, where technology allows knowledge to be mastered in many different ways, non-
Через те, що суб’єкти на ринку праці можуть описувати свої пропозиції або вимоги через неформалізовані характеристики, які formal and informal learning is gaining more significance and importance. At the same time, the opportunities for such
часто є нематеріальними (наприклад, командний дух, соціальні навички, лідерські навички), і для опису таких характеристик learning, both for personal development and professional growth, are practically unlimited either in time or in space. It
можуть використовуватися різні терміни, встановлення діалогу між ними потребує співставлення семантики таких описів. is becoming more and more common to acquire knowledge at work or through participation in the activities of public
Розв’язувати таку проблему мають семантичні технології, які спрямовані на обробку інформації на рівні знань, тобто здатні
формалізувати, аналізувати та обробляти зміст (семантику) інформаційних ресурсів.
organizations, or in the virtual space, both individually and together with others. More often, businesses are offering
Зважаючи на це, у публікації представлено теоретичне обґрунтування системи UkrESCO, призначеної для формуванні паспорту their employees the opportunity to improve the skills they have through organized but non-formal learning.
набутих компетенцій, пошуку вакансій та співставлення компетенцій з вимогами до вакансії на основі моделі ESCO з Acquiring knowledge or skills beyond formal learning and recognizing the outcomes of such learning in the
використанням технологій Semantic Web та інформаційних ресурсів відкритого інформаційного середовища Web. labor market requires the development and testing of appropriate methods, mechanisms and tools. In order to develop
Систему UkrESCO можна розглядати як інтелектуальну надбудову над існуючими системами порівняння та оцінки компетенцій,
співставлення кваліфікацій з вакансіями, формування паспорту набутих компетенцій.
national tools so that the educational services market could approach the labour market, it is advisable to use the global,
Практична реалізація системи UkrESCO може стати ефективним інструментом формування в українському суспільстві розуміння and first of all, European experience.
цінності освіти впродовж життя у особистісному й професійному саморозвитку людини. The European practice of identifying, documenting, evaluating and recognizing the outcomes of non-formal and
Ключові слова: ринок праці, ринок освітніх послуг, професія, знання, навички, компетенції, онтологія, Semantic Web, ESCO. informal learning in the EU member states is aimed at meeting the objectives of the Europe 2020 strategy [1] to achieve
В работе проанализированы публикации, связанные с тенденциями развития национальных систем квалификаций, которые должны
an intellectual, sustainable and comprehensive non-discriminatory growth. Recognizing the outcomes of non-formal
связать рынок образовательных услуг по рынком работы. Такой анализ позволяет определить, что эффективным инструментом для and informal learning has been part of the European political agenda since 2001, when the EU Commission identified
решения этой проблемы является ESCO – Многоязычный классификатор европейских навыков, компетенций, квалификации и lifelong learning as a learning through life activity to improve knowledge, skills, abilities and competences within the
профессий. ESCO определяет и классифицирует навыки (как «мягкие», так и «жесткие»), компетенции, квалификации и профессии, framework of personal, public, social and labour prospects. In 2004, the Common European Recognition Principles
которые имеют значение для европейского рынка работы, образования и профессиональной подготовки. Классификатор ESCO
служит основой для создания «паспорта приобретенных компетенций» и используется в ряде инициатив Европейской Комиссии в
were adopted in the form of the Council Conclusions, and in 2009, the Commission, together with Cedefop, published
сфере навыков и квалификаций, направленных на повышение прозрачности рынка труда и образовательных систем. Разработка European Guidelines for the recognition of non-formal and informal education, which provided politicians and experts
инструментальных средств, которые позволяют эффективно использовать ESCO и обеспечивают переход от квалификационной with technical recommendations on recognition.
модели к полноценных компетентностным моделям, представляется актуальной и своевременной научной задачей. Since the adoption of the Copenhagen Declaration on the expansion of European cooperation in vocational
Из-за того, что субъекты на рынке труда могут описывать свои предложения или требования через неформализованные
характеристики, которые часто являются нематериальными (например, командный дух, социальные привычки, лидерские
education and training, a number of initiatives have been launched to develop European tools for recognizing the
привычки), и для описания таких характеристик могут использоваться различные термины, могут возникать проблемы outcomes of non-formal and informal learning, in particular, in 2004 Europass was established, which includes CV
сопоставления семантики таких описаний. Разрешать такую проблему позволяют семантические технологии, направленные на (Europass-CV) and portfolio of documents which citizens can use to improve reporting of their qualifications and
обработку информации на уровне знаний, то есть способные формализовать, анализировать и обрабатывать смысл (семантику) competences within Europe.
информационных ресурсов.
Учитывая это, в публикации представлено теоретическое обоснование системы UkrESCO, предназначенной для формировании
Despite the launch of the above European initiatives, the progress in recognizing the outcomes of non-formal and
паспорта приобретенных компетенций, поиска вакансий и сопоставления компетенций с требованиями к вакансии на основе informal learning in Europe is uneven and slow, the existing discrepancies between the EU member states further
модели ESCO и с использованием технологий Semantic Web и информационных ресурсов открытой информационной среды Web. restrict the comparability and transparency of recognition systems.
Систему UkrESCO можно рассматривать как интеллектуальную надстройку над существующими системами сравнения и оценки First and foremost, the problem lies in the lack of effective tools for identifying, documenting, evaluating and
компетенций, сопоставление квалификаций с вакансиями, формирование паспорту приобретенных компетенций.
Практическая реализация системы UkrESCO может стать эффективным инструментом формирования в украинском обществе
recognizing the outcomes of non-formal and informal learning that would allow all those who wish to officially
понимания ценности образования в течение жизни в личностном и профессиональном саморазвитии человека. acknowledge their knowledge, skills, abilities and competences, regardless of the conditions in which learning took
Ключевые слова: рынок труда, рынок образовательных услуг, профессия, знания, навыки, компетенции, онтология, Semantic Web, place.
ESCO. Taking into account the above mentioned, the development of tools for identifying, documenting, evaluating and
The paper analyzes publications related to the development trends of national qualifications systems, which should link the market of
recognizing the outcomes of non-formal and informal learning, which allow combining the educational services market
educational services to the labor market. Such an analysis suggests that an effective tool for solving this problem is ESCO – the Multilingual with the labour market, is seen as an urgent and timely scientific task.
Classification of European Skills, Competences, Qualifications and Professionals. ESCO defines and classifies skills (both "soft" and The problem of analyzing the tools for recognizing the outcomes of non-formal and informal learning, which
"hard"), competences, qualifications and occupations that are relevant to the European labor market, education and training. ESCO classifier allows to combine the educational services market with the labour market, ensuring the transition from a qualification
proposes the basis for creating a "passport of acquired competencies". It is used in several European Commission initiatives in the field of
skills and qualifications aimed at increasing the transparency of the labor market and educational systems. The development of tools that
model (confirmation of professional skills by diplomas and certificates on taking training courses) to full competency
allow the effective use of ESCO and ensure the transition from a qualification model to full competency models, is seen as an up-to-date and models, is relevant and requires an urgent solution. This problem is being addressed by both domestic researchers
timely scientific task. (Yu. Borimchuk, L. Boiarchuk, M. Makhsma) and foreign ones (L. Brever, J. James, S. Lins, P. Luksha, D. Pieskov,
Subjects of the labor market can describe their proposals or requirements through non-formalized characteristics that are often non-material M. Afanasiev) [2-6]. In respect of practical implementation, the recognition of the outcomes of non-formal and
(such as team spirit, social skills, leadership skills) and use different terms to describe such characteristics, therefore the problem of
comparing the semantics of such descriptions is occursed. Semantic technologies aimed at the information processing at the knowledge level
informal learning is the focus of both state and private commercial institutions and companies.
(oriented on formalizing, analyzing and processing the semantics of information resources) can solve such a problem. The transition from confirmation of professional skills by diplomas and certificates on taking training courses to
In this regard, the publication presents the theoretical substantiation of UkrESCO designed to create a passport of acquired competencies, to full competency models with the introduction of "passports of acquired competences" will make the process of
search for vacancies and to compare competencies with job requirements based on the ESCO model using Semantic Web technologies and mastering the competences more manageable on the part of the students and will enable them to raise the question of
information resources of the Web open information environment.
the contribution of each educational element in the personal competence profile [7]. However, in order this practice
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could work and models of competences could be recognized by the educational market and the labour market, they must Formulation of the problem
be transnational in nature, and a single platform is needed for their support and development. There are good reasons to The problem of creating electronic tools for describing the educational services market and labour market is
introduce the typology of study certificates (passports, diplomas, certificates) into educational practice, which would greatly complicated due to the increase in the amount of information associated with these services and the complexity
correspond to different competences and qualifications. of data structures used in these electronic media. Effective processing of such information requires its semantization,
One of the first prototypes of the "Digital Diploma" was the Digital Lifelong Diploma, DLD [8]. The idea is to that is, the use of various knowledge bases to determine the context of the search, as well as modern methods and means
capture in a single document all learning outcomes that its owners receive from different sources throughout their lives, of knowledge management. In order to integrate various terminological approaches in various information resources
both official ones, such as Harvard or Michigan, and unofficial ones such as Khan Academy, iTunesU, Coursera, etc. and queries, it is suggested, taking into account the ESCO, to develop an ontological model for the interaction of
The DLD team has already made great progress in developing a digital diploma. They managed to include nearly all of educational institutions, employees and employers, and to create methods for its replenishment with information from
the academic disciplines in America into the catalogue of their platform, and also to catalogue hundreds of unofficial open Web sources – both natural language and semantically marked.
providers of educational services and thousands of courses they have provided.
A significant event in the context of recognition of learning outcomes was the European Commission's support Use of SEMANTIC WEB tools for the labour market
for the VM-Pass, VM project (Virtual Mobility). It was envisaged that the project would support the virtual mobility of People in the labor market can describe their proposals or demands through various non-formalized
students through the creation of an innovative "Learning Passport" [9], a kind of certificate that is filled in by the characteristics that are often non-material (such as team spirit, social skills, leadership skills). Various terms may be
educational institution and an online-student on his/her own, and which is a standard sample where non-formal learning used to describe such characteristics, and therefore a problem arises in comparing the semantics of such descriptions.
and assessment can be documented. In this case, not only subjects and courses are documented, but also independent This problem has to be solved by semantic technologies aimed at processing information at the level of knowledge, that
examinations which were passed, internships and specific skills, such as mastering programming languages, etc. It is, which are capable of formalizing, analyzing and processing the content (semantics) of information resources [13].
would be advisable to include the already achieved practical results in the "passport", in particular the implemented They are based on the use of the knowledge of the subject area for which the task is being solved, and the knowledge
projects, which will make it possible to supplement the professional portfolio, since the taken online courses speak of about the users of these IT, and provide automated analysis of information on the Web. One of the results of such
perseverance, desire for self-study and self-discipline, because not everyone goes through them, although they give an processing is the achievement of the semantic compatibility of information resources (IR), which allows IT-systems to
additional idea of the directions and fields of knowledge the person is most interested in. use and integrate information from different sources and databases. It requires the development of appropriate models,
Despite the significant number of projects implemented owing to the "passport of acquired competences", one methods, languages and technologies.
should keep in mind the lack of tools that would really be able to combine the educational services market and the One of the most popular projects related to the processing of distributed knowledge is Semantic Web [14],
labour market, employers with jobseekers, to combine occupations, qualifications and learning outcomes proposed by the WWW inventor T. Berners-Lee. Semantic Web offers a powerful, practical approach to obtain the tools
(competences). Employers should have access to more accurate and up-to-date information on the skills and of managing large amounts of information and information services [15]. The purpose of this project is to transform the
qualifications of jobseekers in order to understand their professional qualifications better. Learning outcomes are entire set of available IR, accessible through the Web, into a distributed heterogeneous knowledge base. The main
usually defined in terms of knowledge, skills and competences. Common terminology will foster dialogue between the components of Semantic Web are ontologies, Web services and software agents. For their presentation within the
labour market and education, training of those who are interested within and between sectors and borders. In particular, framework of Semantic Web such open standards of knowledge presentation have been developed as the language of
employers will be able to understand better the suitability of the job candidates for the post on the basis of their ontology presentation OWL [16], the IP RDF metadata standard [17] and the query language of SPARQL for these
qualifications, educational services providers will be able to receive feedback about the needs of the labour market, formalized knowledge.
detect gaps in qualifications and adapt them appropriately. In turn, jobseekers will be able to get advice on which Today, the Semantic Web project is actively developing, new languages, standards and tools are emerging, and
qualifications could enhance their employability. existing ones are being improved. Therefore, in the process of developing any information system based on the use of
The European Commission has developed a multilingual classification of European Skills, Competences, the Web resources it is good to focus on these results and create semantic Web services that can effectively take
Qualifications and Occupations (European Skills, Competences, Qualifications and Occupations, ESCO) advantage of the new information environment. The use of ontological analysis provides the ability to transfer
(https://ec.europa.eu/esco/portal/home). The ESCO classification identifies and categorizes skills, competences, knowledge to new applications, the ability of automated export of information from semantically marked IR, and the
qualifications and occupations relevant for the European labour market, education and training [10]. The ESCO unites ability to build a common terminology framework for interaction between different resources and information systems.
both employers wishing to find the right people for their vacancies and jobseekers looking for jobs matching their skills. This is also true for applications aimed at supporting the labour market [18]. Semantic Web technologies easily
The ESCO classification combines labour market and educational services market because, on the one hand, it helps integrate with other modern Web technologies, such as semantic Wiki (for example Semantic MediaWiki).
education providers to understand the labour market needs better and adapt curricula according to the conditions, and, In view of this, we suggest in the course of the system development whose purpose is to draw up a passport of
on the other hand it helps employers understand the learning outcomes that were gained by professionals looking for a acquired competences, to search for vacancies and match competences with job requirements based on the ESCO
job. Equally important is the fact that the ESCO classification combines labour markets from different EU member model, that the following Semantic Web technologies should be used:
countries, allowing jobseekers and employers to deal with skills, training and work more effectively in any European - to personalize user interaction, personal program agents are created for each of the potential employers,
language. jobseekers and individuals who are acquiring or are planning to acquire particular education;
At the basis of the ESCO classification there are three main elements: 1) occupations; 2) knowledge, skills and - the system should provide semantic Web services to support such functions as:
competences; and 3) qualifications. In the ESCO, each concept is related to at least one term in all ESCO languages. In – registration and search for vacancies;
many cases, the language contains more than one term to refer to the same or very similar concepts. Thus, the ESCO – registration and search for CVs;
can contain several terms of one concept. Within the framework of the ESCO data model, each term represents a – comparing vacancies and CVs at the semantic level;
separate element and all of them are related to the concept. This model is based on the Simple Knowledge Organization – search for educational institutions able to provide a certain qualification or education;
System (SKOS) ontology [11]. The ESCO is published as Linked Open Data, and developers can use it in a variety of – comparison of training courses and programs with occupations;
formats (SKOS-RDF, CSV) in programs that provide services such as job search, career guidance and self-esteem. The relationship between occupations, jobs, knowledge, skills, competences and qualifications, between terms
Users can integrate the ESCO classification into their applications and services. In addition, the ESCO provides a local and concepts, etc., as well as between their characteristics, are formalized using ontology (or a set of ontologies
API and API Web services so that applications and Web services could request information from the classification in describing national or regional sets of these concepts).
real time. In addition, the use of knowledge from external ontologies makes it possible to make execution of services more
The practice of using the ESCO classification has showed its effectiveness with a number of international personalized (for example, to take into account the territorial proximity of educational institutions or places of
institutions. In particular, the European Employment Services (EURES – European Employment Services – employment).
http://ec.europa.eu/eures/.) – a network that brings together about 400 "Euro Advisors" from national employment To make such systems sufficiently dynamic and capable of taking into account changes in the surrounding
services, employers' associations, trade unions, local and regional authorities and educational institutions are actively world, one must ensure that you receive information from Web resources. To do this, it is necessary to use intelligent
using the ESCO classification. The EURES portal is a key system for mobility in the EU. EURES has a unified information retrieval systems capable of finding relevant documents by ontological models [19]. To make such a search
information online resource to collect data on the availability of employment vacancies across Europe and provides faster and more automated, it is advisable to focus on processing of semantically-marked information resources, for
European employers and other stakeholders with variety of services and information covering all aspects of recruiting example, Wiki-resources.
from other European countries [12]. The EURES portal is a good example that in recent decades the increased spread of
Web-based technologies has fundamentally changed the way we exchange information in the labour market, and Architecture and model of the UkrESCO
considerably re-oriented it towards the use of electronic means. In the most generalized form the architecture of the UkrESCO looks like this (Fig. 1):
228 229
Database
Database of
External Hierarchic
Employer of the
databases relations
ESCO qualifications
Knowledge
Qualifications
query Database
Ontology of of
the UkrESCO occupations
Skills
Block of
Provider of
qualifications
educational
comparison Passport of services
qualifications
Replenishment Competences Ontologic
of relations
qualifications
Job candidate
Qualifications
Fig.1. Architecture of the UkrESCO
An important element of the UkrESCO is its ontological model.
Fig.2. Ontology of the UkrESCO
robji ,x k , rdata j ,dm , , where
Ontological analysis is an effective means for modeling representations about various subject areas (SA) which
allows to display their semantics [20]. In order to formalize the basic concepts of the ESCO and the relationship Each instance of the information objects (IO) x X can be represented as
between them, we will use the following formal model of ontology O X, R, F, T , where X X cl X ind is a set of
robji – the object properties of the subject area ontology, rdata j – the properties of the subject area ontology data, x k
basic concepts of ontology, X cl is a set of classes, Xind is a set of their instances; R rier _ cl {ri } {p j} a set of –
is optional instances of different classes of the IO, d m – constants of different types. Each robj can be considered as
relations between classes and instances of ontology classes, where rier _ cl – hierarchic relations between ontology i
robji : X in i ,..., X in i X out i ,..., X out i , that is, for each object property, the region value and area of determination
classes and class properties; {ri } – a set of object properties of class instances; {p j} – a set of properties of the given 1 k 1 m
from subsets of the IO are determined.
instances of classes; F – a set of characteristics of ontology classes, instances of classes and their properties; T – a set of
data types [21]. Instances of different classes in the UkrESCO are associated with different object properties robj . The object
i
From the point of view of the problem being solved, the main classes of the UkrESCO ontology are competence; property of an association has no additional constraints (such as transitivity, symmetry, etc.) and therefore does not
occupation; qualification, as well as those subjects that may be associated with these classes: owners (potential reflect additional semantics that allows them to be presented in OWL Light language. In addition, the UkrESCO uses
employees with specific knowledge and skills), contractors (employers) and providers (individuals and organizations semantically loaded object properties such as "requires prior learning", "based on education level", etc., which may
providing educational services that allow to increase qualifications). have additional restrictions.
The main purpose of the UkrESCO creation is to improve the interaction between subjects related to
qualifications. The UkrESCO can be considered as an intellectual superstructure over existing systems of comparison
and assessment of competences, matching qualifications with vacancies, forming a passport of acquired competences. Block of
However, in order to correctly determine the relationship between them and ensure their comparison, the classes qualifications Database of
associated with all the basic terms of the UkrESCO are introduced into this ontology. comparison qualifications
Thus, the UkrESCO ontology contains the following classes X cl (the list of classes is ordered in alphabetical
order, but not by significance): knowledge, jobseeker, qualification, competence, country, course, skill, educational The UkrESCO
program, course provider, occupation, job, employer. If necessary, these classes are specified and supplemented by Block of
Ontology
subclasses and properties. For example, skills are divided into "soft" and "hard", into the main and additional ones. search
Database of
The use of an ontological model allows us to establish the relationships between these classes clearly and
occupations
unambiguously and ensure their unified common understanding [22]. It is important that such a model fixes not only
Vacancy
hierarchic relations {ri } , but also specific for the subject area connections {p j} . For example, you can clearly indicate
that the employer specifies the qualifications that a job candidate must hold, and the education passport for the job Course
candidate can be changed by the course provider adding additional skills and qualifications to the education passport Passport of
associated with that candidate. Using Protégé ontology editor allows you to visualize these relationships in a way that is qualifications Information
understandable to the users of the system (Fig. 2). Instances of some classes X ind are added to the ontological model search Web
engine
when developing the UkrESCO. For example, instances of "Skills" contain the elements imported from the ESCO. The
ontological model is replenished by other instances in the process of system operation. For example, these are job
candidates' profiles, employers' requests, and educational services providers' proposals. This model is described in Fig.3. Use of the UkrESCO ontology by search engines
OWL Light language and can be visualized by means of Protégé's Ontology Editor. OWL Lite (just like OWL DL and
OWL 2.0) are based on descriptive logic ALC (Attributive Language with Complements), which guarantees the Interaction of the UkrESCO with search engines
completeness of logical output on this ontology. The model describes the properties of classes (both object properties An important feature of the UkrESCO is the ability to search for new resources in the information space of the
and data properties) and the relationship between the basic terms and their subclasses [23]. Web. In order to automate this search, it is suggested to use the knowledge of the subject area in which this system
operates (Fig. 3). This knowledge is formalized in the form of the UkrESCO ontology, and therefore it can be applied
without any additional processing in the systems of semantic search, oriented to the use of ontologies, for example, in
[25].
230 231
It is this ontology that allows you to describe what information objects you need to find on the Web, identifying In this regard, the publication presents the theoretical substantiation of the UkrESCO system designed to create a
their structure and the necessary elements. For example, you can search for potential employees (ontology class passport of acquired competences, to search for vacancies and to compare competences with job requirements based on
"educational program"), educational services (ontology class "course provider") and employers (ontology class the ESCO model using Semantic Web technologies and information resources of the Web open information
"vacancy"). It is clear that such results will be much less reliable than those obtained inside the UkrESCO from environment.
registered users, but the availability of such a search can significantly expand the system's capabilities and ensure its The UkrESCO system can be considered as an intellectual superstructure over existing systems of comparison
connection with the open environment. This property is a characteristic difference from most similar systems. It and assessment of competences, matching qualifications with vacancies, forming a passport of acquired competences.
provides obtaining information not only from relevant databases, but also from all the unstructured Web content. This Practical implementation of the UkrESCO system may become an effective tool for the formation of
opportunity should be used to find only those vacancies and CVs that occur very rarely. understanding of the value of lifelong learning in the personal and professional self-development of a person in
Ukrainian society.
Interaction of the UkrESCO with Wiki-resources
Due to the fact that obtaining information from the unstructured Web resources (even with the help of semantic Literature
search) requires human involvement to filter out the obtained results, it is advisable to provide the UkrESCO with 1. Олейникова О.Н., Муравьева А.А., Аксёнова Н.М. Обучение в течение всей жизни как инструмент реализации Лиссабонской
стратегии. М.: РИО ТК им. Коняева, 2009 131с.
semantically-marked resources. The most widespread among these resources today are resources organized on the basis
2. Борімчук Ю.М. Реалізація стратегії сприяння зайнятості молоді в Європі. Ринок працi та зайнятість населення. 4(45). 2015. C. 50–53.
of Wiki-technology and which are semantically expanded. This is due to the fact that users can easily replenish and 3. Боярчук Л.В. Застосування зарубіжного досвіду в роботі Державної служби зайнятості України. Науковий вісник Полісся № 1 (1),
update such Wiki pages, and their semantic markup can be easily transformed into a corresponding ontology. 2015. С. 65–70.
Existing tools for ontological analysis allow you to make a comparison between Wiki-ontology (that is, the 4. Махсма М. Світові тенденції трансформації зайнятості населення в умовах глобалізації економіки. Україна: аспекти праці. 2007. № 4.
С. 10–15.
ontology whose elements are the basis of the semantic markup of the Wiki resource) and the ontology of the UkrESCO.
5. Лукша П., Песков Д., Афанасьев М. и др. Будущее образования: глобальная повестка. С. : Сколковский институт науки и технологий.
On the basis of such a comparison, you can find pertinent pages and obtain not only information on CVs and vacancies 2016. 196 с.
from them, but also more important information – information about occupations, specialties and courses. Usually, the 6. Линс С. С «MOOC» или не с «MOOC»? Учеба будущего. http://vzagranke.ru/razvitie/grani/global/s-mooc-ili-ne-s-mooc-ucheba-
update of the Wiki resources is executed more frequently compared with the updating of knowledge bases and budushhego.html.
7. Рекомендації ради про визнання неформального й інформального навчання. Брюсель : Комісія Європейського союзу, 2012. 21 с.
ontologies because it can be done not only by knowledge engineers but also by ordinary users who are knowledgeable
8. Образовательный сервис Degreed – новый подход к высшему образованию. http://www.aleksandr.kz/resursyi/founding-scholars-
in the relevant subject area. jailbreaking-the-degree.html#.WOPsNWekIdU.
Wiki-ontology is an ontology built on a semantically marked Wiki resource (a set of Wiki pages containing 9. У Європі розроблений єдиний атестат онлайн-освіти. http://osvita.ua/abroad/higher_school/distance-learning/44715/.
semantic markup) [21, 25]. It contains only the knowledge that can be directly obtained from the semantic markup. 10. European Commission, 2013. ESCO: European Classification of Skills/Competences, Qualifications and Occupations,
http://bookshop.europa.eu/en/esco-european- classification-of-skills-competences-qualifications-and- occupations-pbKE0313496.
Therefore, in this ontology there are no, for example, such characteristics of classes and properties as equivalence, lack
11. Miles A., Bechhofer S. SKOS Simple Knowledge Organization System Reference, World Wide Web Consortium (W3C) recommendation,
of intersection, etc. 2009. www.w3.org/TR/skos-reference.
In this model, a set of concepts is constructed as a combination of such Wiki elements as pages and categories 12. Європейська політика і соціальне партнерство в ЄС: довідник / Н.Г. Діденко, І.Я. Тодоров, О.Р. Чугріна, О.К. Міхеєва / за заг. ред. Н.Г.
X X wiki _ categor X wiki _ page associated with different types of relationships with Діденко. Донецьк: ДонДУУ, 2012. 150 с.
13. Lundqvist K.O., Baker K.D., Williams S.A. An ontological approach to competency management, 2008. http://www.eife-
l.org/publications/proceedings/ilf07/ Contribution110.doc.pdf.
R {r ier _ cl } {r link } {rsem _ prop } : a set of classes is a set of Wiki categories X wiki _ categor , between which 14. W3C Semantic Web Activity. http://www.w3.org/2001/sw/Activity/.
15. Warren P. Knowledge Management and the Semantic Web: From Scenario to Technology. IEEE Intelligent Systems. 21, 1, 53–59, 2006.
there are hierarchic relations; a set of instances is a set of Wiki-pages X wiki _ page , between which there are references 16. OWL Web Ontology Language Semantics and Abstract Syntax. Section 2. Abstract Syntax. http://www.w3. org/ TR/owl-semantics/syntax.html.
17. Lassila O., Swick R. Resource Description Framework (RDF) Model and Syntax Specification, W3C Recommendation.
rlink and semantic relations rsem _ propi , i 0, m ; a set of data types is supplemented by a specific class – "Wiki-page". http://www.w3.org/TR/REC-rdf-syntax.
18. De Smedt J., le Vrang M., Papantoniou A. ESCO: Towards a Semantic Web for the European Labor Market. LDOW@ WWW, 2015.
This model can be upgraded with such Wiki elements as templates, forms, custom pages, and more. http://ceur-ws.org/Vol-1409/paper-10.pdf.
When searching for pertinent Wiki-pages, you need to compare classes X wiki _ categor and semantic properties 19. Rogushina Julia V., Pryima Serhii M. Ontological approach to qualifications matching on base of competences: model and methods. Науковий
вісник НГУ, 2017, № 6. С. 162–168.
rsem _ prop i , i 0, m of Wiki-pages with classes X cl and object properties rier _ cl : X cl X cl of the UkrESCO 20. Rogushina Julia V., Pryima Serhii M. Development of methods for support of qualification frameworks transparency based on semantic
technologies. Information Technologies and Learning Tools. 2017. Vol. 59. N 3. P. 201–210. (Web Of Science) Available at:
ontology. If they match (or the level of compliance is higher than the specified rating), then the UkrESCO ontology set http://journal.iitta.gov.ua/index.php/itlt/ article/view/1655/1201.
must be replenished with an instance corresponding to the found Wiki-page, that is, to create an instance of the 21. Rogushina J. Analysis of Automated Matching of the Semantic Wiki Resources with Elements of Domain Ontologies. International Journal of
Mathematical Sciences and Computing(IJMSC). 2017. Vol. 3, N 3. P. 50–58.
specified class in which the value of the object properties is of the value derived from this Wiki-page . 22. Rogushina J., Priyma S. Use of Competence Ontological Model for Matching of Qualifications. Chemistry: Bulgarian Journal of Science
It is important that such replenishment can be performed completely autonomously and does not require any Education. 2017. Vol. 26. N 2. P. 216–228.
efforts of users and developers of the UkrESCO. 23. Rogushina Julia, Pryima Serhii. The use of ontologies and semantic web to provide for the transparency of qualifications frameworks. Eastern-
The same Wiki resources can be found by external search engines or recommended by developers of the European Journal of Enterprise Technologies. 2017. Vol. 1, N 2 (85). P. 25–31. http://journals.uran.ua/eejet/article/view/92815.
24. Rogushina J.V. The Use of Ontological Knowledge for Semantic Search of Complex Information Objects. Open semantic technologies for
UkrESCO based on thematic relevance and high level of trust. An example of such a resource is the electronic version intelligent systems, 2017. P. 127–132.
of the Great Ukrainian Encyclopedia. 25. Rogushina J. Semantic Wiki resources and their use for the construction of personalized ontologies CEUR Workshop Proceedings. 1631. 2016.
Similarly, you can use non-semantic Wiki resources (for example, Wikipedia), taking into account the P. 188–195.
categorization of pages, but in this case, the replenishment of the UkrESCO ontology requires human involvement to
determine the properties of the instance.
Conclusions and prospects of further research References
The analysis of the publication made it possible to conclude that an effective tool that allows to combine the
educational services market with the labour market is the Multilingual Classification of European Skills, Competences, 1. Oleynikova O.N. & Muraveva A.A. & Akyonova N.M. (2009) Life-long learning as an instrument for implementing the Lisbon Strategy. - M .:
Qualifications and Occupations (the ESCO). The classification of ESCO identifies and classifies skills (both "soft" and RIO TK Konyaev, 131 p.(in Russian)
"hard"), competences, qualifications and occupations that are relevant to the European labour market, education and 2. Borimchuk YU.M. (2015) Implementation of the Strategy for Promoting Youth Employment in Europe // Labor Market and Employment of the
Population, 4 (45), P. 50–53. (in Ukrainian)
professional training. The ESCO classification is the basis for creating a "passport of acquired competences" and is used 3. Boyarchuk L.V. (2015) Application of foreign experience in the work of the State Employment Service of Ukraine / L.V. Boyarchuk //
in several European Commission initiatives in the area of skills and qualifications aimed at increasing the Scientific Bulletin of Polissya, N 1 (1). P. 65–70. (in Ukrainian)
transparency of the labour market and educational systems. Since people in the labour market can describe their 4. Mahsma M. (2007) Global trends in the transformation of employment in the conditions of globalization of the economy // Ukraine: aspects of
proposals or demands through non-formalized characteristics that are often non-material (such as team spirit, social labor, N 4. P. 10–15. (in Ukrainian)
5. Luksha P.& Peskov D. & Afanasyev M. (2016) The future of education: global agenda / P. Luksha, and others - S.: Skolkovo Institute of
skills, leadership skills) and different terms may be used to describe such characteristics, there occurs the problem of Science and Technology, 196 p. (in Russian)
comparing the semantics of such descriptions. This problem is to be solved by semantic technologies aimed at 6. Lins S. With "MOOC" or not with "MOOC"? Learning of the future. (in Russian) Available from: http://vzagranke.ru/razvitie/grani/global/s-
processing information at the level of knowledge, that is, which are capable of formalizing, analyzing and processing mooc-ili-ne-s-mooc-ucheba-budushhego.html.
the content (semantics) of information resources.
232 233
7. Recommendations of the Council for the recognition of non-formal and informal learning (2012), Brussels: Commission of the European ladamandraka2010@gmail.com,
Union. 21 p. (in Ukrainian)
oksana.strokan@tsatu.edu.ua
8. Educational Service Degreed - a New Approach to Higher Education. (in Russian) Available from: http://www.aleksandr.kz/resursyi/founding-
scholars-jailbreaking-the-degree.html#.WOPsNWekIdU.
9. Integrated certificate of online education is developed in Europe. (in Ukrainian) Available from: http://osvita.ua/abroad/higher_school/distance-
learning/44715/.
10. European Commission (2013). ESCO: European Classification of Skills/Competences, Qualifications and Occupations,
http://bookshop.europa.eu/en/esco-european- classification-of-skills-competences-qualifications-and- occupations-pbKE0313496.
11. MILES A. & BECHHOFER S. (2009) SKOS Simple Knowledge Organization System Reference, World Wide Web Consortium (W3C)
recommendation, . – www.w3.org/TR/skos-reference.
12. European policy and social partnership in the EU: handbook / N.G. Didenko, I.Ya. Todorov, O.R. Chugrin, O.K. Mikheev / Ed. By N.G.
Didenko - Donetsk: DonNUU, 2012. 150 p. (in Ukrainian)
13. Lundqvist K.O. & Baker K.D. & Williams S.A. (2008) An ontological approach to competency management. Available from:
http://www.eife-l.org/publications/proceedings/ilf07/ Contribution110.doc.pdf .
14. W3C Semantic Web Activity. Available from: http://www.w3.org/2001/sw/Activity/.
15. Warren P. (2006) Knowledge Management and the Semantic Web: From Scenario to Technology. IEEE Intelligent Systems. 21, 1, 53–59.
16. OWL Web Ontology Language Semantics and Abstract Syntax. Section 2. Abstract Syntax. Available from: http://www.w3. org/ TR/owl-
semantics/syntax.html.
17. Lassila O. & Swick R. Resource Description Framework (RDF) Model and Syntax Specification, W3C Recommendation. Available from:
http://www.w3.org/TR/REC-rdf-syntax.
18. DE Smedt J. & LE Vrang M. & Papantoniou A. (2015) ESCO: Towards a Semantic Web for the European Labor Market. LDOW@ WWW, .
Available from: http://ceur-ws.org/Vol-1409/paper-10.pdf.
19. Pryima S. & Rogushina J. (2017) Ontological approach to qualifications matching on base of competences: model and methods // Scientific
bulletin of NGU, N 6. P. 162–168.
20. Rogushina J. & Priyma S. (2017) Development of methods for support of qualification frameworks transparency based on semantic
technologies / Information Technologies and Learning Tools, Vol. 59, N 3. P. 201–210. Available from:
http://journal.iitta.gov.ua/index.php/itlt/ article/view/1655/1201.
21. Rogushina J. (2017) Analysis of Automated Matching of the Semantic Wiki Resources with Elements of Domain Ontologies // International
Journal of Mathematical Sciences and Computing(IJMSC), Vol. 3, N 3. P. 50–58.
22. Rogushina J. & Priyma S. Use of Competence Ontological Model for Matching of Qualifications // Chemistry: Bulgarian Journal of Science
Education, 2017, Vol. 26, №2. – P. 216–228.
23. Rogushina J. & Pryima S. (2017) The use of ontologies and semantic web to provide for the transparency of qualifications frameworks //
Eastern-European Journal of Enterprise Technologies, 2017, Vol. 1, N 2 (85). – P. 25–31. Available from:
http://journals.uran.ua/eejet/article/view/92815.
24. Rogushina J.V. (2017) The Use of Ontological Knowledge for Semantic Search of Complex Information Objects // Open semantic technologies
for intelligent systems, 2017. P. 127–132.
25. Rogushina J. (2016) Semantic Wiki resources and their use for the construction of personalized ontologies // CEUR Workshop Proceedings
1631. P. 188–195.
Про авторів:
Прийма Сергій Миколайович,
доктор педагогічних наук, доцент
Кількість наукових публікацій в українських виданнях – 43.
Кількість наукових публікацій в зарубіжних виданнях – 15.
http://orcid.org/0000-0002-2654-5610,
Рогушина Юлія Віталіївна,
кандидат фізико-математичних наук,
старший науковий співробітник Інституту програмних систем НАН України.
Кількість наукових публікацій в українських виданнях – 140.
Кількість наукових публікацій в зарубіжних виданнях – 30.
Індекс Хірша – 10.
http://orcid.org/0000-0001-7958-2557,
Строкань Оксана Вікторівна,
кандидат технічних наук, доцент, доцент кафедри комп’ютерних наук.
http://orcid.org/0000-0002-6937-3548-id
Місце роботи авторів:
Таврійський державний агротехнологічний університет,
проспект Б. Хмельницького, 18, Мелітополь, Україна,
Інститут програмних систем НАН України,
03181, Київ-187, проспект Академіка Глушкова, 40,
Тел.: +38 (097) 2814372,
(066) 550 1999.
E-mail: pryima.serhii@gmail.com,
234 235