Linguistic Ontologies: Designing and Using in the Educational Intellectual Systems
ЕЛЕКТРОННІ РЕСУРСИ
ТА ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНІ ТЕХНОЛОГІЇ
ELECTRONIC RESOURCES AND INFORMATION
AND COMMUNICATION TECHNOLOGIES
ЭЛЕКТРОННЫЕ РЕСУРСЫ
И ИНФОРМАЦИОННО‐КОММУНИКАЦИОННЫЕ ТЕХНОЛОГИИ
UDC 81’4:004.89
DOI: 10.31866/2617-796X.4.1.2021.236950
Tkachenko Olha,
PhD in Physical and Mathematical Sciences,
Associate Professor, Department of Information Technologies and Design,
State University of Infrastructure and Technology,
Kyiv, Ukraine
https://orcid.org/0000-0003-1800-618X
Tkachenko Kostiantyn,
PhD in Economics,
Associate Professor, Department of Information Technologies and Design,
State University of Infrastructure and Technology,
Kyiv, Ukraine
https://orcid.org/0000-0003-0549-3396
Tkachenko Oleksandr,
PhD in Physical and Mathematical Sciences,
Associate Professor, Department of Software Engineering,
National Aviation University,
Kyiv, Ukraine
https://orcid.org/0000-0001-6911-2770
LINGUISTIC ONTOLOGIES: DESIGNING AND USING IN THE EDUCATIONAL
INTELLECTUAL SYSTEMS
The purpose of the article is to investigate and consider the general trends, problems and
prospects of designing and using linguistic ontologies in educational intellectual systems.
The research methodology consists in semantic analysis methods of the basic concepts in the
considered subject area (linguistic ontologies in the educational intellectual systems). The article
discusses approaches to the use of linguistic models in modern educational intelligent systems.
© Tkachenko О. І.
© Tkachenko К. О.
© Tkachenko О. А.
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Цифрова платформа: інформаційні технології в соціокультурній сфері
2021, Том 4 № 1
The novelty of the research is the analysis of the linguistic ontologies use in the educational intellectual systems.
Conclusions. A model of linguistic ontology for the domain (disciplines “Computer Networks” and “Modelling Systems”) is presented. This model is used in the development of an
educational intellectual system that supports online learning in these disciplines. The proposed
model describes a set of relations of linguistic ontology, specially selected to describe the analyzed domain. To ensure these properties, it was proposed to use a small set of relationships.
The proposed linguistic ontological model is implemented in an educational intelligent system
that supports such disciplines as “Computer Networks” and “Modelling Systems”.
Keywords: educational intellectual system; domain; model; ontology; linguistic ontology;
information resource.
Introduction. Modern educational intellectual systems work with textual information and knowledge of domains, which include thousands of different classes of entities that are among themselves in a huge number of different types of relationships
(Liu, 2017; Greger and Porshnev, 2013).
Processing information and knowledge in such systems are often guided by the
use of statistical characteristics of this information and knowledge:
–– frequency of occurrence of words in educational `s materials, tests, reference
information, glossaries, etc.;
–– frequency of joint occurrence of words.
–– Users of educational intellectual systems (lecturers, methodologists, students),
performing text educational information and knowledge processing, primarily:
–– reveals the main content of the educational documents and the meaning of its
key concepts;
–– the main topic, subtopics and key concepts of the educational documents
(training materials, tests, reference information, glossaries, etc.).
For this, the user of educational intellectual systems usually uses a large amount
of knowledge about:
–– linguistic knowledge – a language of presentation of training materials, tests,
reference information;
–– ontological knowledge – domain;
–– relations between units of linguistic knowledge – organization of the coherent
text.
Lack of linguistic and ontological knowledge leads to a variety of problems when,
for example:
–– formulating queries differ from templates of describing relevant educational
information and knowledge that are supported by educational intellectual systems;
–– long requests are processed (for example, when referring to help information);
–– the context of the language is not fully taken into account.
Thus, modern educational intellectual systems for processing text information
face the following problems (Scherer, 2016):
–– processing of text information of online courses in the considered domain;
–– taking into account the linguistic features of the language and the structure of
the corresponding educational or test`s text.
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Цифрова платформа: інформаційні технології в соціокультурній сфері
2021, Том 4 № 1
These problems are in the educational intellectual systems. Intellectual text analysis is one of the key tasks in the field of artificial intelligence associated with the
problems of automatic analysis and synthesis of natural language arising from the
interaction of users with educational intellectual systems.
The solution to these problems is closely related to the use of various approaches
of artificial intelligence and computational linguistics.
Ontological modelling and computer learning methods have made it possible for
practical use in natural language processing tasks in the educational intellectual systems.
The use of linguistic and ontological knowledge in the automatic processing of
texts in educational intellectual systems is a difficult problem.
This is due to the fact that such knowledge should be described in specially created
thesauri (ISO 25964-1:2011, 2011) and linguistic ontologies, which should contain descriptions of a lot of words and phrases and be able to logically derive new knowledge.
The paper considers the extraction of information from the text, which can be
used to create formal models of specific areas of linguistic knowledge. In work, this
is the area of online courses in the disciplines “Computer networks” and “Modelling
systems”.
Model based on distribution semantics determines the semantic similarity between two linguistic elements (words or phrases) based on their distribution properties in large fragments of educational material or tests without specific knowledge of
the lexical or grammatical meanings of the elements.
A word set is a collection of documents in the form of a matrix, the rows of which
correspond to the documents, and the columns to a specific term. Intersection values
describe the number of words in a particular educational document.
These models often include a weight for each term – document pair. The indicator
is the frequency of occurrences of a term in each educational document or the probability of finding a word in an educational document.
This rates the more general words as more important, although this is not always
the case. One of the paradigms of computer resources for educational intellectual
systems are formal ontologies (for example, the Semantic Web) (O. Тkachenko, А. Тkachenko and К. Тkachenko, 2020; Munira and Anjumb, 2018; Sowa, 2009; Web Ontology Language). But the aut (...truncated)
