@ARTICLE{Denisov2021256,
author={Denisov, M. and Anikin, A. and Sychev, O. and Katyshev, A.},
title={Program execution comprehension modelling for algorithmic languages learning using ontology-based techniques},
journal={Advances in Intelligent Systems and Computing},
year={2021},
volume={1184},
pages={256-269},
doi={10.1007/978-981-15-5859-7_25},
url={https://link.springer.com/chapter/10.1007%2F978-981-15-5859-7_25},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation; Software Engineering School, Volgograd, Russian Federation},
abstract={In this paper, we propose an ontology-based approach to model a program execution comprehension so to be able to explain to the novice programmer the essence of his/her error. We have studied the algorithmic languages model operating with actions and basic control structures (“sequence,” “branching,” and “looping”) and designed the rules to capture any deviation from the permissible. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.},
correspondence_address1={Anikin, A.; Software Engineering SchoolRussian Federation; эл. почта: Anton@Anikin.name},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={21945357},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
thanks = {rfbr-18-07-00032}
}

@article{Anikin_2019_2,
	doi = {10.1088/1757-899x/483/1/012073},
	url = {https://doi.org/10.1088%2F1757-899x%2F483%2F1%2F012073},
	year = 2019,
	month = {mar},
	publisher = {{IOP} Publishing},
	volume = {483},
	pages = {012073},
	author = {A Anikin and A Katyshev and M Denisov and V Smirnov and D Litovkin},
	title = {Using online update of distributional semantics models for decision-making support for concepts extraction in the domain ontology learning task},
	journal = {{IOP} Conference Series: Materials Science and Engineering},
	thanks = {rfbr-18-07-00032},
	abstract = {Most of the information processed by computer systems is presented in the form of text corpuses. The number of such texts (as well as the corpus as a whole) only increases with time, and therefore the word processing tasks remain relevant to this day. Ontology allows to describe semantics using domain concepts and relations between them [1, 2]. In the ontology learning task, the ontology is dependent on quality of corpus which may not be readily available. There are different approaches to creating ontologies (including the use of different tools and frameworks). This paper discusses the use of word2vec (group of related models that are used to produce word embeddings) using online vocabulary update and extension of the original data corpus with additional training for the domain concepts extraction to automate the domain ontology creation.}
}