Semantic Search, Ontology-based Information Retrieval, Ad-hoc Object Retrieval, Knowledge Based Models, Software Assistive Technology
Education and research experience
January 2013 – current:
Assoc. Prof. at the Software Engineering Department, Faculty of electronics and computer science, Volgograd State Technical University, Volgograd, Russia
January 2003 — December 2012:
Sr. Lecturer at the Computer-aided Design Department and Software Engineering Department, Faculty of electronics and computer science, Volgograd State Technical University, Volgograd, Russia
January 2000 – January 2003:
PhD student at the Software Engineering Department, Faculty of electronics and computer science, Volgograd State Technical University, Volgograd, Russia
Research Topic: Automatic control of functional state in organic system.
Scientific Adviser: Prof. Alexander M. Dvoryankin.
September 1994 – December 1999:
University graduate at the Faculty of electronics and computer science, Volgograd State Technical University, Russia (graduate thesis: “The system for modeling and analysis of technical object behavior based on the quality model”)
Software modeling and analysis, Software design, Software construction, Information retrieval, Databases
Course organizer and lecturer:
2003 - 2017: Programming Languages Basics
2005 – 2007: Artificial Intelligence
2006 - 2017: Object-Oriented Analysis and Programming
2012 - 2015: Computer science foundations
2012 – 2016: Software Construction
2012 - 2017: Structured Programming
2015 - 2017: Object-Oriented Modeling and Design with UML
Semantic Web and Information Retrieval, Mobile Game Programming 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++, Java, UML 2.0, С#, OWL, SQL
Databases: StarDog, MySQL
Technologies and tools: Microsoft Visual Studio, QT Library, NetBeans, Unity, Git, Mercurial, Protege
Authored over 73 papers, including 4 papers listed in Scopus and Web of Science and 26 listed in Russian SCI
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.
D. Litovkin, D. Dontsov, A. Anikin, O. Sychev (2021)
Suitability of Object-Role Modeling Diagrams as an Intermediate Model for Ontology Engineering: Testing the Rules for Mapping.
Advances in Intelligent Systems and Computing 1310 pp. 188-194. Springer Science and Business Media Deutschland GmbH.
doi web bibtex @ARTICLE{Litovkin2021188,
author={Litovkin, D. and Dontsov, D. and Anikin, A. and Sychev, O.},
title={Suitability of Object-Role Modeling Diagrams as an Intermediate Model for Ontology Engineering: Testing the Rules for Mapping},
journal={Advances in Intelligent Systems and Computing},
year={2021},
volume={1310},
pages={188-194},
doi={10.1007/978-3-030-65596-9_24},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-65596-9_24},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
abstract={Creating and understanding ontologies using OWL2 language is a hard, time-consuming task for both domain experts and consumers of knowledge (for example, teachers and students). Using Object-Role Modeling diagrams as an intermediate model facilitates this process. To achieve this, the method of mapping ORM2 diagrams to OWL2 ontologies and vice versa is necessary. Such methods were proposed in different works, but their suitability and possible errors are in doubt. In this paper, we propose a method of evaluating how well existing rules of mapping follow ORM semantics. Several ontologies were created using mapping rules and tested. During testing, a significant difference between ORM2 and OWL2 basic properties and assumptions were discovered. This difference require updating the mapping rules. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
correspondence_address1={Anikin, A.; Volgograd State Technical UniversityRussian Federation; эл. почта: anton@anikin.name},
editor={Samsonovich A.V., Gudwin R.R., Simoes A.d.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={21945357},
isbn={9783030655952},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
thanks = {rfbr-20-07-00764}
}
Abstract Creating and understanding ontologies using OWL2 language is a hard, time-consuming task for both domain experts and consumers of knowledge (for example, teachers and students). Using Object-Role Modeling diagrams as an intermediate model facilitates this process. To achieve this, the method of mapping ORM2 diagrams to OWL2 ontologies and vice versa is necessary. Such methods were proposed in different works, but their suitability and possible errors are in doubt. In this paper, we propose a method of evaluating how well existing rules of mapping follow ORM semantics. Several ontologies were created using mapping rules and tested. During testing, a significant difference between ORM2 and OWL2 basic properties and assumptions were discovered. This difference require updating the mapping rules. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
2020 (1)
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 (3)
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, 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.
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.
2018 (1)
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"}
2017 (2)
M. Kultsova, D. Litovkin, I. Zhukova, A. Dvoryankin (2017)
Intelligent support of decision making in management of large-scale systems using case-based, rule-based and qualitative reasoning over ontologies.
Communications in Computer and Information Science 754 pp. 331-349. Springer Verlag.
doi web bibtex @ARTICLE{Kultsova2017331,
author={Kultsova, M. and Litovkin, D. and Zhukova, I. and Dvoryankin, A.},
title={Intelligent support of decision making in management of large-scale systems using case-based, rule-based and qualitative reasoning over ontologies},
journal={Communications in Computer and Information Science},
year={2017},
volume={754},
pages={331-349},
doi={10.1007/978-3-319-65551-2_24},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029418378&doi=10.1007%2f978-3-319-65551-2_24&partnerID=40&md5=6fa75ba7fa4e36ec9cda99997702f9eb},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
abstract={The current trend in intelligent support of decision making is an integration of different knowledge representation models and reasoning mechanisms, it allows improving quality and efficiency of obtained decisions. In this paper, we present an ontology-based approach to intelligent support of decision making in the management of large-scale systems using case-based, rule-based and qualitative reasoning. A concept of the reasoning mechanisms integration implies that case-based reasoning (CBR) takes on the role of leading reasoning mechanism, while rule-based (RBR) and qualitative reasoning (QR) support the different phases of CBR-cycle - adaptation and revision phases respectively. The paper describes a modified CBR-cycle and ontological knowledge representation model which supports the proposed concept of reasoning integration. A formal qualitative model of decision making was developed for revision of case solution, it includes the following components: system state model, action model, and assessment model. An ontological representation of the qualitative model was proposed for integration with structural case model in an ontological knowledge base. Implementation of the proposed approach is illustrated by a number of examples of decision making support in various subject domains. © Springer International Publishing AG 2017.},
author_keywords={Intelligent support of decision making; Knowledge intensive case based reasoning; Ontological case representation; Qualitative model},
keywords={Artificial intelligence; Case based reasoning; Integration; Knowledge based systems; Knowledge representation; Large scale systems; Ontology, Case representation; Casebased reasonings (CBR); Decision making support; Intelligent support; Ontological representation; Qualitative model; Qualitative reasoning; Reasoning mechanism, Decision making},
correspondence_address1={Kultsova, M.; Volgograd State Technical UniversityRussian Federation; эл. почта: marina.kultsova@mail.ru},
editor={Groumpos P., Kravets A., Shcherbakov M., Kultsova M.},
publisher={Springer Verlag},
issn={18650929},
isbn={9783319655505},
language={English},
abbrev_source_title={Commun. Comput. Info. Sci.},
document_type={Conference Paper},
source={Scopus},
}
Abstract The current trend in intelligent support of decision making is an integration of different knowledge representation models and reasoning mechanisms, it allows improving quality and efficiency of obtained decisions. In this paper, we present an ontology-based approach to intelligent support of decision making in the management of large-scale systems using case-based, rule-based and qualitative reasoning. A concept of the reasoning mechanisms integration implies that case-based reasoning (CBR) takes on the role of leading reasoning mechanism, while rule-based (RBR) and qualitative reasoning (QR) support the different phases of CBR-cycle - adaptation and revision phases respectively. The paper describes a modified CBR-cycle and ontological knowledge representation model which supports the proposed concept of reasoning integration. A formal qualitative model of decision making was developed for revision of case solution, it includes the following components: system state model, action model, and assessment model. An ontological representation of the qualitative model was proposed for integration with structural case model in an ontological knowledge base. Implementation of the proposed approach is illustrated by a number of examples of decision making support in various subject domains. © Springer International Publishing AG 2017.
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 (2)
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 (1)
Irina Zhukova, Marina Kultsova, Dmitry Litovkin, Dmitry Kozlov (2015)
Generation of OWL Ontologies from Confinement Models. In
Creativity in Intelligent, Technologies and Data Science: First Conference, CIT&DS 2015, Volgograd, Russia, September 15–17, 2015, Proceedings. pp. 191–203. Springer International Publishing. Cham.
doi web bibtex @Inbook{Zhukova2015,
author="Zhukova, Irina
and Kultsova, Marina
and Litovkin, Dmitry
and Kozlov, Dmitry",
editor="Kravets, Alla
and Shcherbakov, Maxim
and Kultsova, Marina
and Shabalina, Olga",
title="Generation of OWL Ontologies from Confinement Models",
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="191--203",
isbn="978-3-319-23766-4",
doi="10.1007/978-3-319-23766-4_16",
url="http://dx.doi.org/10.1007/978-3-319-23766-4_16"
}
2014 (2)
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"
}
Dmitry Litovkin, Irina Zhukova, Marina Kultsova, Natalia Sadovnikova, Alexander Dvoryankin (2014)
Adaptive Testing Model and Algorithms for Learning Management System. In
Knowledge-Based Software Engineering: 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014. Proceedings. pp. 87–99. Springer International Publishing. Cham.
doi web bibtex @Inbook{Litovkin2014,
author="Litovkin, Dmitry
and Zhukova, Irina
and Kultsova, Marina
and Sadovnikova, Natalia
and Dvoryankin, Alexander",
editor="Kravets, Alla
and Shcherbakov, Maxim
and Kultsova, Marina
and Iijima, Tadashi",
title="Adaptive Testing Model and Algorithms for Learning Management System",
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="87--99",
isbn="978-3-319-11854-3",
doi="10.1007/978-3-319-11854-3_9",
url="http://dx.doi.org/10.1007/978-3-319-11854-3_9"
}