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
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 «Intelligent support of decision making in management of large scale systems on the base of integration of different types of reasoning on ontological knowledge», 2015-2017.
Programming languages: C++, Python, SQL, OWL, SWRL, Linux shell
Databases: MySQL, NoSQL (Redis, MongoDB), StarDog
Technologies and tools: Protege, LaTeX, Git, SVN
Authored over 35 papers, including 6 papers listed in Scopus and Web of Science and 18 listed in Russian SCI
2019 (1)
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"
}
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 (3)
A. Anikin, M. Kultsova, D. Litovkin, E. Sarkisova (July 2018)
Representation of WHAT-Knowledge Structures as Ontology Design Patterns. In
2018 9th International Conference on Information, Intelligence, Systems Applications (IISA).
bibtex @INPROCEEDINGS{IISA2018_1,
author={A. Anikin and M. Kultsova and D. Litovkin and E. Sarkisova},
booktitle={2018 9th International Conference on Information, Intelligence, Systems Applications (IISA)},
title={Representation of WHAT-Knowledge Structures as Ontology Design Patterns},
year={2018},
month={July},}
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 Applications (IISA).
bibtex @INPROCEEDINGS{IISA2018_2,
author={M. Kultsova and D. Matyushechkin and A. Anikin},
booktitle={2018 9th International Conference on Information, Intelligence, Systems Applications (IISA)},
title={Web-service for translation of pictogram messages into Russian coherent text},
year={2018},
month={July},}
M. Kultsova, A. Potseluico, A. Usov, 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 Applications (IISA).
bibtex @INPROCEEDINGS{IISA2018_3,
author={M. Kultsova and A. Potseluico and A. Usov and A. Anikin},
booktitle={2018 9th International Conference on Information, Intelligence, Systems Applications (IISA)},
title={An Ontological Representation of Interface Patterns in Context of Interface Adaptation for Users with Special Needs},
year={2018},
month={July},}
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
