Anton Anikin

@article{Anikin_2019_1,
	doi = {10.1088/1757-899x/483/1/012074},
	url = {https://doi.org/10.1088%2F1757-899x%2F483%2F1%2F012074},
	year = 2019,
	month = {mar},
	publisher = {{IOP} Publishing},
	volume = {483},
	pages = {012074},
	author = {Anton Anikin and Dmitry Litovkin and Elena Sarkisova and Tatyana Petrova and Marina Kultsova},
	title = {Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research},
	journal = {{IOP} Conference Series: Materials Science and Engineering},
	thanks = {rfbr-18-07-00032, rfbr-18-47-340014},
	abstract = {The paper presents an approach to knowledge management in learning and scientific research that allows increasing the availability of expert knowledge and reducing semantic noise during knowledge transfer. The availability of expert knowledge is ensured by transforming them from implicit into explicit form (in the form of Semantic Link Network). The reduction of semantic noise is achieved through the integration of knowledge in different forms and their personalization for different groups of knowledge recipients. In the paper, the tasks of decision-making support are formulated which should be performed by the knowledge management system on the base of the proposed approach.}
}

@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.}
}

@InProceedings{10.1007/978-3-319-99316-4_1,
author="Anikin, Anton
and Sychev, Oleg
and Gurtovoy, Vladislav",
editor="Samsonovich, Alexei V.",
title="Multi-level Modeling of Structural Elements of Natural Language Texts and Its Applications",
booktitle="Biologically Inspired Cognitive Architectures 2018",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="1--8",
abstract="Methods of extracting knowledge in the analysis of large volumes of natural language texts are relevant for solving various problems in the field of analysis and generation of textual information, such as text analysis for extracting data, fact and semantics; presenting extracted information in a convenient for machine processing form (for example, ontology); classification and clustering texts, including thematic modeling; information retrieval (including thematic search, search based on the user model, ontology-based models, document sample based search); texts abstracting and annotating; developing of intelligent question-answering systems; generating texts of different types (fiction, marketing, weather forecasts etc.); as well as rewriting texts, preserving the meaning of the original text for presenting it to different target audiences. In order for such methods to work, it is necessary to construct and use models that adequately describe structural elements of the text on different levels (individual words, sentences, thematic text fragments), their characteristics and semantics, as well as relations between them, allowing to form higher-level structures. Such models should also take into account general characteristics of textual data: genre, purpose, target audience, scientific field and others. In this paper, authors review three main approaches to text modeling (structural, statistical and hybrid), their characteristics, pros and cons and applicability on different stages (knowledge extraction, storage and text generation) of solving problems in the field of analysis and generation of textual information.",
isbn="978-3-319-99316-4",
thanks = "rfbr-18-07-00032"
}

@article{Matyushechkin2018,
title = "Web-service for Translation Pictogrammes Sages into Coherent Text in Russian",
journal = "Известия Волгоградского государственного технического университета",
number="5",
volume = "215",
pages = "30 - 36",
year = "2018",
issn = "1990-5297",
url = "https://elibrary.ru/item.asp?id=34991077",
author = "Dmitrii Matyushechkin, and Marina Kultsova, and Anton Anikin",
abstract="This article examines the implementation of web-service providing translation of pictogram messages into text messages in Russian. Also approaches to the decision of the given task are considered and results of development of methods of translation are presented. The developed web-service for the translation of pictogram messages into text messages can have a wide range of applications in the field of augmentative and alternative communication for people with mental and speech disorders. Also, this web service can be used by third-party software developers for people with disabilities, which will enable their programs to translate pictogram messages.",
thanks="rfbr-18-07-00032"
}

@ARTICLE{Anikin2017133,
author={Anikin, A. and Litovkin, D. and Kultsova, M. and Sarkisova, E. and Petrova, T.},
title={Ontology visualization: Approaches and software tools for visual representation of large ontologies in learning},
journal={Communications in Computer and Information Science},
year={2017},
volume={754},
pages={133-149},
doi={10.1007/978-3-319-65551-2_10},
note={cited By 0; Конференция 2nd Conference on Creativity in Intelligent Technologies and Data Science, CIT and DS 2017 ; Дата конференции: с 12 September 2017 по 14 September 2017;  Код конференции:197189},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029446521&doi=10.1007%2f978-3-319-65551-2_10&partnerID=40&md5=afe223b4bf23023f708f73a0259ee0ae},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation; Volgograd State Socio-Pedagogical University, Volgograd, Russian Federation},
abstract={In this paper, we address the issue of large ontologies visualization for learning. The ontologies can be used to improve the efficiency of the learning when the learner explores a new subject domain and needs its conceptual model. The ontologies can help to overview this new subject domain, to better understand current knowledge, the knowledge that should be got, the subject domain structure, main concepts, relationships between them and relevant information resources. However with increasing the complexity of domain structure, the number of concepts and relationships, it becomes more difficult to overview and understand conceptual model represented with the ontology. We review the approaches to ontology visualization and their implementation in the software tools that can help to resolve this issue. © Springer International Publishing AG 2017.},
author_keywords={Ontology;  Ontology visualization;  Semantic web},
keywords={Computer software;  Semantic Web;  Visualization, Conceptual model;  Domain structure;  Information resource;  Ontology visualizations;  Visual representations, Ontology},
correspondence_address1={Kultsova, M.; Volgograd State Technical UniversityRussian Federation; эл. почта: marina.kultsova@mail.ru},
editor={Groumpos P., Kravets A., Shcherbakov M., Kultsova M.},
sponsors={},
publisher={Springer Verlag},
issn={18650929},
isbn={9783319655505},
language={English},
abbrev_source_title={Commun. Comput. Info. Sci.},
document_type={Conference Paper},
source={Scopus},
}