Web Search, User Models, Federated Search, Distributed Information Retrieval, Ontology-based Information Retrieval, Ontology Learning, NLP, ML, Human Computer Interaction.
Education and research experience
June 2016 – current:
Ass. Prof. at the Software Engineering Department, Faculty of electronics and computer science, Volgograd State Technical University, Volgograd, Russia
September 2006 — June 2016:
Sr. Lecturer at the Software Engineering Department, Faculty of electronics and computer science, Volgograd State Technical University, Volgograd, Russia
April 2014 – current:
Postdoctoral Researcher at the Software Engineering Department, Faculty of electronics and computer science, Volgograd State Technical University, Volgograd, Russia
Research Topic: Intelligent support of decision making in
management of large scale systems on the base of integration of different types of reasoning on ontological knowledge.
September 2006 – December 2014:
PhD student at the Software Engineering Department, Faculty of electronics and computer science, Volgograd State Technical University, Volgograd, Russia
Research Topic: Ontology-based method of heterogeneous distributed electronic learning resources retrieval and integration.
Scientific Adviser: Prof. Alexander M. Dvoryankin. Scientific Consultant: Irina G. Zhukova
September 2000 – July 2006:
BSc and MSc at the Faculty of electronics and computer science, Volgograd State Technical University, Russia (MSc thesis: «Models and tools for integration of the distributed resources into the open educational networks»)
Information Retrieval, Algorithms and Data Structures, Databases, Software Engineering, Human Computer Interaction, User Modelling
-
-
-
ESSIR 2017
-
KESW 2016
BICA 2018
IISA 2015, 2016, 2018
ICICT 2019
MSc supervision: 4 students,
BSc supervision: 6 students.
Course organizer and lecturer:
Teaching assistant:
Semantic Web and Information Retrieval workshops.
Grants and scholarships
RFBR grant 15-07-03541 (2015-2017) «Intelligent support of decision making in management of large scale systems on the base of integration of different types of reasoning on ontological knowledge».
RFBR grant 18-07-00032 (2018-2020): Intelligent support of decision making of knowledge management for learning and scientific research based on the collaborative creation and reuse of the domain information space and ontology knowledge representation model.
RFBR grant 18-47-340014 (2018-2019): Development of the mechanism of semantic zooming for the ontology geometric OWL graph to increase the efficiency of decision making in the tasks of learning a new domain, storage and sharing knowledge.
Programming languages: C++, Python, SQL, OWL, SWRL, Linux shell
Databases: MySQL, NoSQL (Redis, MongoDB), StarDog
Technologies and tools: Protege, LaTeX, Git, SVN
Authored over 40 papers, including 8 papers listed in Scopus and Web of Science and 29 listed in Russian SCI
2019 (5)
Anton Anikin, Oleg Sychev (jul 2019)
Ontology-Based Modelling for Learning on Bloom's Taxonomy Comprehension Level. In
Advances in Intelligent Systems and Computing. pp. 22–27. Springer International Publishing.
doi web bibtex @incollection{Anikin2019_2,
doi = {10.1007/978-3-030-25719-4_4},
url = {https://doi.org/10.1007/978-3-030-25719-4_4},
year = {2019},
month = jul,
publisher = {Springer International Publishing},
pages = {22--27},
author = {Anton Anikin and Oleg Sychev},
title = {Ontology-Based Modelling for Learning on Bloom's Taxonomy Comprehension Level},
booktitle = {Advances in Intelligent Systems and Computing}
}
Oleg Sychev, Anton Anikin, Artem Prokudin (jul 2019)
Methods of Determining Errors in Open-Ended Text Questions. In
Advances in Intelligent Systems and Computing. pp. 521–526. Springer International Publishing.
doi web bibtex @incollection{Anikin2019_3,
doi = {10.1007/978-3-030-25719-4_67},
url = {https://doi.org/10.1007/978-3-030-25719-4_67},
year = {2019},
month = jul,
publisher = {Springer International Publishing},
pages = {521--526},
author = {Oleg Sychev and Anton Anikin and Artem Prokudin},
title = {Methods of Determining Errors in Open-Ended Text Questions},
booktitle = {Advances in Intelligent Systems and Computing}
}
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.
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.
2017 (1)
A. Anikin, D. Litovkin, M. Kultsova, E. Sarkisova, T. Petrova (2017)
Ontology visualization: Approaches and software tools for visual representation of large ontologies in learning.
Communications in Computer and Information Science 754 pp. 133-149. Springer Verlag.
doi web bibtex @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},
}
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.
2016 (5)
Marina Kultsova, Anastasiya Potseluico, Anton Anikin, Roman Romanenko (Nov 2016)
An Ontology Based Adaptation of User Interface for People with Special Needs. In
2016 IEEE Artificial Intelligence and Natural Language Conference (AINL). pp. 92-94.
bibtex @INPROCEEDINGS{7891861,
booktitle={2016 IEEE Artificial Intelligence and Natural Language Conference (AINL)},
author = {Kultsova, Marina and Potseluico, Anastasiya and Anikin, Anton and Romanenko, Roman},
title={An Ontology Based Adaptation of User Interface for People with Special Needs},
year={2016},
pages={92-94},
keywords={Artificial intelligence;Business intelligence;Natural language processing;Organizations;Seminars;Speech},
month={Nov},}
M. Kultsova, R. Rudnev, A. Anikin, I. Zhukova (July 2016)
An ontology-based approach to intelligent support of decision making in waste management. In
2016 7th International Conference on Information, Intelligence, Systems Applications (IISA). pp. 1-6.
doi bibtex @INPROCEEDINGS{7785401,
author={M. Kultsova and R. Rudnev and A. Anikin and I. Zhukova},
booktitle={2016 7th International Conference on Information, Intelligence, Systems Applications (IISA)},
title={An ontology-based approach to intelligent support of decision making in waste management},
year={2016},
pages={1-6},
keywords={decision making;decision support systems;environmental science computing;inference mechanisms;ontologies (artificial intelligence);waste management;contemporary technologies;domain knowledge representation;intelligent decision making support;knowledge-based approach;ontology-based approach;rule-based reasoning;waste management system;Cognition;Decision making;Electronic mail;Knowledge based systems;Ontologies;Recycling;Waste management},
doi={10.1109/IISA.2016.7785401},
month={July},}
M. Kultsova, A. Potseluico, A. Anikin, R. Romanenko (July 2016)
An ontological user model for automated generation of adaptive interface for users with special needs. In
2016 7th International Conference on Information, Intelligence, Systems Applications (IISA). pp. 1-6.
doi bibtex @INPROCEEDINGS{7785411,
author={M. Kultsova and A. Potseluico and A. Anikin and R. Romanenko},
booktitle={2016 7th International Conference on Information, Intelligence, Systems Applications (IISA)},
title={An ontological user model for automated generation of adaptive interface for users with special needs},
year={2016},
pages={1-6},
keywords={diseases;handicapped aids;inference mechanisms;ontologies (artificial intelligence);user modelling;SWLR-rules;application interface domain ontology;automated adaptive interface generation;disease ontology;interface adaptation;ischemic stroke;meta-ontology;ontological user model;rule-based reasoning;user device domain ontology;user disability domain ontology;Adaptation models;Cognition;Diseases;Mobile communication;Ontologies;User interfaces;Visualization},
doi={10.1109/IISA.2016.7785411},
month={July},}
M. Kultsova, A. Anikin, D. Litovkin (February 2016)
An Ontology-Based Approach to Collaborative Development of Domain Information Space. In
Proceedings of the 12th International Conference on Educational Technologies (EDUTE`16), Proceedings of the 10th International Conference on Business Administration (ICBA`16), Barcelona, Spain, February 13-15, 2016. pp. 13-19.
web bibtex @INPROCEEDINGS{WSEAS2016,
author={M. Kultsova and A. Anikin and D. Litovkin},
booktitle={Proceedings of the 12th International Conference on Educational Technologies (EDUTE`16), Proceedings of the 10th International Conference on Business Administration (ICBA`16), Barcelona, Spain, February 13-15, 2016},
title={An Ontology-Based Approach to Collaborative Development of Domain Information Space},
year={2016},
pages={13-19},
month={February},
url={http://www.wseas.us/e-library/conferences/2016/barcelona/EDBA/EDBA-01.pdf},
}
Anton Anikin, Dmitry Litovkin, Marina Kultsova, Elena Sarkisova (2016)
Ontology-Based Collaborative Development of Domain Information Space for Learning and Scientific Research. In
Knowledge Engineering and Semantic Web: 7th International Conference, KESW 2016, Prague, Czech Republic, September 21-23, 2016, Proceedings. pp. 301–315. Springer International Publishing. Cham.
doi web bibtex @Inbook{Anikin2016,
author="Anikin, Anton
and Litovkin, Dmitry
and Kultsova, Marina
and Sarkisova, Elena",
editor="Ngonga Ngomo, Axel-Cyrille
and K{\v{r}}emen, Petr",
title="Ontology-Based Collaborative Development of Domain Information Space for Learning and Scientific Research",
bookTitle="Knowledge Engineering and Semantic Web: 7th International Conference, KESW 2016, Prague, Czech Republic, September 21-23, 2016, Proceedings",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="301--315",
isbn="978-3-319-45880-9",
doi="10.1007/978-3-319-45880-9_23",
url="http://dx.doi.org/10.1007/978-3-319-45880-9_23"
}
2015 (2)
M. Kultsova, A. Anikin, I. Zhukova (July 2015)
Ontology-based method of electronic learning resources retrieval and integration. In
2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). pp. 1-6.
doi bibtex @INPROCEEDINGS{7388112,
author={M. Kultsova and A. Anikin and I. Zhukova},
booktitle={2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)},
title={Ontology-based method of electronic learning resources retrieval and integration},
year={2015},
pages={1-6},
keywords={C++ language;computer aided instruction;distributed processing;information retrieval;ontologies (artificial intelligence);C++ language;Volgograd State Technical University;distributed learning resources;electronic learning resources integration;electronic learning resources retrieval;ontology-based method;personal learning collections;programming languages;Cognition;Complexity theory;Electronic learning;Erbium;Metadata;Ontologies;Semantics},
doi={10.1109/IISA.2015.7388112},
month={July},}
Marina Kultsova, Anton Anikin, Irina Zhukova, Alexander Dvoryankin (2015)
Ontology-Based Learning Content Management System in Programming Languages Domain. In
Creativity in Intelligent, Technologies and Data Science: First Conference, CIT&DS 2015, Volgograd, Russia, September 15–17, 2015, Proceedings. pp. 767–777. Springer International Publishing. Cham.
doi web bibtex @Inbook{Kultsova2015,
author="Kultsova, Marina
and Anikin, Anton
and Zhukova, Irina
and Dvoryankin, Alexander",
editor="Kravets, Alla
and Shcherbakov, Maxim
and Kultsova, Marina
and Shabalina, Olga",
title="Ontology-Based Learning Content Management System in Programming Languages Domain",
bookTitle="Creativity in Intelligent, Technologies and Data Science: First Conference, CIT{\&}DS 2015, Volgograd, Russia, September 15--17, 2015, Proceedings",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="767--777",
isbn="978-3-319-23766-4",
doi="10.1007/978-3-319-23766-4_61",
url="http://dx.doi.org/10.1007/978-3-319-23766-4_61"
}
2014 (1)
Anton Anikin, Marina Kultsova, Irina Zhukova, Natalia Sadovnikova, Dmitry Litovkin (2014)
Knowledge Based Models and Software Tools for Learning Management in Open Learning Network. In
Knowledge-Based Software Engineering: 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014. Proceedings. pp. 156–171. Springer International Publishing. Cham.
doi web bibtex @Inbook{Anikin2014,
author="Anikin, Anton
and Kultsova, Marina
and Zhukova, Irina
and Sadovnikova, Natalia
and Litovkin, Dmitry",
editor="Kravets, Alla
and Shcherbakov, Maxim
and Kultsova, Marina
and Iijima, Tadashi",
title="Knowledge Based Models and Software Tools for Learning Management in Open Learning Network",
bookTitle="Knowledge-Based Software Engineering: 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014. Proceedings",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="156--171",
isbn="978-3-319-11854-3",
doi="10.1007/978-3-319-11854-3_15",
url="http://dx.doi.org/10.1007/978-3-319-11854-3_15"
}
Assoc. Prof., PhD
