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The project is aimed at solving the fundamental scientific problem of organizing intellectual support of decision making for knowledge management in the educational process and in the framework of scientific research based on modern technologies of artificial intelligence.
As part of the learning process, this problem is relevant because of:
- the large amount of accumulated electronic learning resources that meet the requirements of the educational process and the individual requirements of various students;
- the complexity of creating educational content;
- the possibility of using such resources in an adaptive learning process, taking into account the general and individual requirements for such content and educational purposes.
The problems of constructing a model of the domain structure, retrieval and integration of distributed open scientific information resources into thematic collections, creating information spaces including such resources with the possibility of joint access and development are also relevant in scientific research.
Existing approaches are limited to the search for educational content, do not take into account individual requirements, do not allow managing the learning process on a variety of learning resources and do not allow creating smart learning content based on the integration of learning resources in accordance with general and individual requirements. In support of research processes, existing approaches are limited to the implementation of tools for joint editing of scientific articles with subsequent publication in the public domain, the means of creating repositories of abstracts of scientific articles, as well as modeling tools for subject areas without taking into account the specific nature of research processes.
Within the framework of the proposed research, it is proposed to develop a new concept of creating an information space of a subject domain on the basis of the cognitive space of information process subjects using ontological models of knowledge representation and algorithms based on logical inference on ontology to support the decision making for the knowledge management in this space.
As a result of the project, it is planned to implement the proposed concept in the form of a methodology for intellectual support of decision making for knowledge management in the educational process in this subject area and scientific research with the support of personalization, collective creation and reuse of information space objects.
Direct analogs of the proposed approach to intellectual support of decision making for knowledge management in the educational process and scientific research does not exist.
2021 (2)
D V Litovkin, A V Anikin, O A Sychev, T Petrova (feb 2021)
ORM Diagram as an Intermediate Model for OWL Ontology Engineering: Protégé ORM Plugin Implementation.
Journal of Physics: Conference Series 1801 (1) pp. 012009. IOP Publishing.
doi web bibtex @article{Litovkin_2021,
doi = {10.1088/1742-6596/1801/1/012009},
url = {https://doi.org/10.1088/1742-6596/1801/1/012009},
year = 2021,
month = {feb},
publisher = {{IOP} Publishing},
volume = {1801},
number = {1},
pages = {012009},
author = {D V Litovkin and A V Anikin and O A Sychev and T Petrova},
title = {{ORM} Diagram as an Intermediate Model for {OWL} Ontology Engineering: Prot{\'{e}}g{\'{e}} {ORM} Plugin Implementation},
journal = {Journal of Physics: Conference Series},
abstract = {OWL2, a widely-used ontology-representation language, is poorly perceived by humans because OWL2 statements have a low level of abstraction. To solve this issue, various OWL2 ontology editors are used, which allow to group statements and represent them using some visual notation. ORM-diagram is a good candidate for an intermediate model for authoring and understanding of OWL2-ontology as Object-Role Modelling notation supports visually distinguishable constructs, has the high expressive capabilities, and implements the node-link paradigm and the attribute-free approach A Protégé plugin allowing to create an ORM2-diagram using the live error checking approach was implemented. The plugin allows us to form a valid object-oriented diagram model in computer memory using a widely known ontology authoring tool Protégé.},
thanks = {rfbr-18-07-00032, rfbr-20-07-00764}
}
Abstract OWL2, a widely-used ontology-representation language, is poorly perceived by humans because OWL2 statements have a low level of abstraction. To solve this issue, various OWL2 ontology editors are used, which allow to group statements and represent them using some visual notation. ORM-diagram is a good candidate for an intermediate model for authoring and understanding of OWL2-ontology as Object-Role Modelling notation supports visually distinguishable constructs, has the high expressive capabilities, and implements the node-link paradigm and the attribute-free approach A Protégé plugin allowing to create an ORM2-diagram using the live error checking approach was implemented. The plugin allows us to form a valid object-oriented diagram model in computer memory using a widely known ontology authoring tool Protégé.
M. Denisov, A. Anikin, O. Sychev, A. Katyshev (2021)
Program execution comprehension modelling for algorithmic languages learning using ontology-based techniques.
Advances in Intelligent Systems and Computing 1184 pp. 256-269. Springer Science and Business Media Deutschland GmbH.
doi web bibtex @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}
}
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.
2020 (2)
Oleg Sychev, Anton Anikin, Artem Prokudin (2020)
Automatic grading and hinting in open-ended text questions.
Cognitive Systems Research 59 pp. 264 - 272.
doi web bibtex @article{SYCHEV2020,
title = "Automatic grading and hinting in open-ended text questions",
journal = "Cognitive Systems Research",
volume = "59",
pages = "264 - 272",
year = "2020",
issn = "1389-0417",
doi = "https://doi.org/10.1016/j.cogsys.2019.09.025",
url = "http://www.sciencedirect.com/science/article/pii/S1389041719304978",
author = "Oleg Sychev and Anton Anikin and Artem Prokudin",
thanks = {rfbr-18-07-00032}
}
Dmitry Litovkin, Anton Anikin, Marina Kultsova (2020)
Interactive Visualization of Ontology-Based Conceptual Domain Models in Learning and Scientific Research. In
Fourth International Congress on Information and Communication Technology. pp. 365–374. Springer Singapore. Singapore.
doi bibtex @InProceedings{10.1007/978-981-15-0637-6_31,
doi="10.1007/978-981-15-0637-6_31",
author="Litovkin, Dmitry
and Anikin, Anton
and Kultsova, Marina",
editor="Yang, Xin-She
and Sherratt, Simon
and Dey, Nilanjan
and Joshi, Amit",
title="Interactive Visualization of Ontology-Based Conceptual Domain Models in Learning and Scientific Research",
booktitle="Fourth International Congress on Information and Communication Technology",
year="2020",
publisher="Springer Singapore",
address="Singapore",
pages="365--374",
abstract="The paper presents an approach to knowledge transferring and sharing on the base of the semantic link network (SLN) representing expert knowledge in the explicit form. To provide an efficient SLN understanding, it is represented with a geometric graph which can be interactively visualized using a combination of the appropriate visualization methods. The coupling of these methods allows getting a different level of details of the SLN visualization in accordance with the user needs. The proposed approach is planned being implemented in the knowledge management system for learning and scientific research.",
isbn="978-981-15-0637-6",
thanks = {rfbr-18-07-00032, rfbr-18-47-340014}
}
Abstract The paper presents an approach to knowledge transferring and sharing on the base of the semantic link network (SLN) representing expert knowledge in the explicit form. To provide an efficient SLN understanding, it is represented with a geometric graph which can be interactively visualized using a combination of the appropriate visualization methods. The coupling of these methods allows getting a different level of details of the SLN visualization in accordance with the user needs. The proposed approach is planned being implemented in the knowledge management system for learning and scientific research.
2019 (5)
Anton Anikin, Dmitry Litovkin, Elena Sarkisova, Tatyana Petrova, Marina Kultsova (mar 2019)
Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research.
IOP Conference Series: Materials Science and Engineering 483 pp. 012074. IOP Publishing.
doi web bibtex @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.}
}
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.
A Anikin, A Katyshev, M Denisov, V Smirnov, D Litovkin (mar 2019)
Using online update of distributional semantics models for decision-making support for concepts extraction in the domain ontology learning task.
IOP Conference Series: Materials Science and Engineering 483 pp. 012073. IOP Publishing.
doi web bibtex @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.}
}
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.
Marina Kultsova, Anastasiya Potseluico, Alexander Dvoryankin (2019)
Ontology Based Personalization of Mobile Interfaces for People with Special Needs. In
Creativity in Intelligent Technologies and Data Science. pp. 422–433. Springer International Publishing. Cham.
doi bibtex @InProceedings{10.1007/978-3-030-29750-3_33,
doi="10.1007/978-3-030-29750-3_33",
author="Kultsova, Marina
and Potseluico, Anastasiya
and Dvoryankin, Alexander",
editor="Kravets, Alla G.
and Groumpos, Peter P.
and Shcherbakov, Maxim
and Kultsova, Marina",
title="Ontology Based Personalization of Mobile Interfaces for People with Special Needs",
booktitle="Creativity in Intelligent Technologies and Data Science",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="422--433",
abstract="The paper is devoted to a problem of interface personification for people with special needs on the base of information about their behavior during interaction with mobile applications. This work evolves our previous researches on the development of adaptive user interfaces. Much attention in this paper was given to the investigation of existing approaches to collecting and analyzing the information about user behavior and interaction context data as well as interface adaptation recommendations. The improved interface adaptation mechanism was developed and described based on the ontological representation of the interface patterns and knowledge about users and their interaction with a mobile application. The set of adaptation rules was developed and implemented in the ontology knowledge base. In the paper, we described the improved ontology model and some examples of ontological representation of interface patterns.",
isbn="978-3-030-29750-3",
thanks = {rfbr-18-07-00032}
}
Abstract The paper is devoted to a problem of interface personification for people with special needs on the base of information about their behavior during interaction with mobile applications. This work evolves our previous researches on the development of adaptive user interfaces. Much attention in this paper was given to the investigation of existing approaches to collecting and analyzing the information about user behavior and interaction context data as well as interface adaptation recommendations. The improved interface adaptation mechanism was developed and described based on the ontological representation of the interface patterns and knowledge about users and their interaction with a mobile application. The set of adaptation rules was developed and implemented in the ontology knowledge base. In the paper, we described the improved ontology model and some examples of ontological representation of interface patterns.
Dmitry Litovkin, Anton Anikin, Marina Kultsova (2019)
Semantic Zooming Approach to Semantic Link Network Visualization. In
Creativity in Intelligent Technologies and Data Science. pp. 81–95. Springer International Publishing. Cham.
doi bibtex @InProceedings{10.1007/978-3-030-29750-3_7,
doi="10.1007/978-3-030-29750-3_7",
author="Litovkin, Dmitry
and Anikin, Anton
and Kultsova, Marina",
editor="Kravets, Alla G.
and Groumpos, Peter P.
and Shcherbakov, Maxim
and Kultsova, Marina",
title="Semantic Zooming Approach to Semantic Link Network Visualization",
booktitle="Creativity in Intelligent Technologies and Data Science",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="81--95",
abstract="In the paper, we described a semantic zooming approach to the visualization of special kind structures - semantic link networks (SLN), represented as a visual graph. The proposed approach allows decreasing semantic noise in SLN overview and navigation and also simplifies the process of understanding the domain studied with SLN by means of semantic zooming. We proposed priori importance levels of SLN items and semantic zooming scale to visualize the SLN with different details level. We designed an interactive SLN visualization process including the following SLN transformations: filtering SLN items, context collapse and expansion for SLN item, and changing the details in the visualized object representation in the geometric SLN graph. The transformation algorithms were developed, and also examples of SNL semantic zooming were described in details in the paper.",
isbn="978-3-030-29750-3",
thanks = {rfbr-18-07-00032, rfbr-18-47-340014}
}
Abstract In the paper, we described a semantic zooming approach to the visualization of special kind structures - semantic link networks (SLN), represented as a visual graph. The proposed approach allows decreasing semantic noise in SLN overview and navigation and also simplifies the process of understanding the domain studied with SLN by means of semantic zooming. We proposed priori importance levels of SLN items and semantic zooming scale to visualize the SLN with different details level. We designed an interactive SLN visualization process including the following SLN transformations: filtering SLN items, context collapse and expansion for SLN item, and changing the details in the visualized object representation in the geometric SLN graph. The transformation algorithms were developed, and also examples of SNL semantic zooming were described in details in the paper.
Anton Anikin, Oleg Sychev, Vladislav Gurtovoy (2019)
Multi-level Modeling of Structural Elements of Natural Language Texts and Its Applications. In
Biologically Inspired Cognitive Architectures 2018. pp. 1–8. Springer International Publishing. Cham.
bibtex @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"
}
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.
2018 (5)
D. Litovkin, A. Anikin, M. Kultsova, E. Sarkisova (July 2018)
Representation of WHAT-Knowledge Structures as Ontology Design Patterns. In
2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA). pp. 1-6.
doi bibtex @INPROCEEDINGS{8633701,
author={D. {Litovkin} and A. {Anikin} and M. {Kultsova} and E. {Sarkisova}},
booktitle={2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA)},
title={Representation of WHAT-Knowledge Structures as Ontology Design Patterns},
year={2018},
volume={},
number={},
pages={1-6},
keywords={Unified modeling language;Ontologies;Taxonomy;Visualization;Mediation;OWL},
doi={10.1109/IISA.2018.8633701},
ISSN={},
month={July},
thanks = "rfbr-18-07-00032"}
M. Kultsova, D. Matyushechkin, A. Anikin (July 2018)
Web-Service for Translation of Pictogram Messages into Russian Coherent Text. In
2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA). pp. 1-5.
doi bibtex @INPROCEEDINGS{8633677,
author={M. {Kultsova} and D. {Matyushechkin} and A. {Anikin}},
booktitle={2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA)},
title={Web-Service for Translation of Pictogram Messages into Russian Coherent Text},
year={2018},
volume={},
number={},
pages={1-5},
keywords={Machine learning;Neural networks;Natural languages;Training;Servers;Python;Task analysis},
doi={10.1109/IISA.2018.8633677},
ISSN={},
month={July},
thanks = "rfbr-18-07-00032"}
M. Kultsova, A. Usov, A. Potseluico, A. Anikin (July 2018)
An Ontological Representation of Interface Patterns in Context of Interface Adaptation for Users with Special Needs. In
2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA). pp. 1-5.
doi bibtex @INPROCEEDINGS{8633682,
author={M. {Kultsova} and A. {Usov} and A. {Potseluico} and A. {Anikin}},
booktitle={2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA)},
title={An Ontological Representation of Interface Patterns in Context of Interface Adaptation for Users with Special Needs},
year={2018},
volume={},
number={},
pages={1-5},
keywords={Ontologies;Cognition;Adaptation models;Mobile applications;Databases;Engines;Software},
doi={10.1109/IISA.2018.8633682},
ISSN={},
month={July},
thanks = "rfbr-18-07-00032"}
Anton Anikin, Oleg Sychev (2018)
Semantic treebanks and their uses for multi-level modelling of natural-language texts.
Procedia Computer Science 145 pp. 64 - 71.
doi web bibtex @article{ANIKIN201864,
title = "Semantic treebanks and their uses for multi-level modelling of natural-language texts",
journal = "Procedia Computer Science",
volume = "145",
pages = "64 - 71",
year = "2018",
note = "Postproceedings of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2018 (Ninth Annual Meeting of the BICA Society), held August 22-24, 2018 in Prague, Czech Republic",
issn = "1877-0509",
doi = "10.1016/j.procs.2018.11.011",
url = "http://www.sciencedirect.com/science/article/pii/S1877050918322968",
author = "Anton Anikin and Oleg Sychev",
keywords = "Treebanks, Natural Language Processing, Ontology, Text Modelling",
thanks = "rfbr-18-07-00032"
}
Dmitrii Matyushechkin, Marina Kultsova, Anton Anikin (2018)
Web-service for Translation Pictogrammes Sages into Coherent Text in Russian.
Известия Волгоградского государственного технического университета 215 (5) pp. 30 - 36.
web bibtex @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"
}
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.