Oleg Sychev

@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}
}

@CONFERENCE{Sychev2021728,
author={Sychev, O.},
title={Combining neural networks and symbolic inference in a hybrid cognitive architecture},
journal={Procedia Computer Science},
year={2021},
volume={190},
pages={728-734},
doi={10.1016/j.procs.2021.06.085},
url={https://www.sciencedirect.com/science/article/pii/S1877050921013405?via%3Dihub},
affiliation={Volgograd State Technical University, Lenin Ave, 28, Volgograd, 400005, Russian Federation},
abstract={Recently, there has been a big progress in developing artificial deep-learning neural networks and large-scale knowledge graphs. However, the results in these two research fields have serious drawbacks. The solutions offered by neural networks remain unstable and prone to adversarial attacks: while the percentage of correct answers increases, the incorrect answers often contain glaring errors. Large knowledge graphs contain a lot of facts but little knowledge; they are mostly used for information search and retrieval. The ability to reason conclusions on them is limited, and the majority of modern research turns to approximate methods like neural networks and graph embeddings to draw conclusions and use the accumulated knowledge. In this work, I propose a hybrid cognitive architecture inspired by the observable features of human thinking. Pruning obviously wrong solutions seems to be more natural for human symbolic reasoning than making far-fetched strict logical conclusions, while generating new ideas is often intuitive. So a hybrid cognitive architecture can employ generative neural networks as a sort of "intuition" (generating possible solutions) and symbolic inference as a control contour to verify and filter the generated solutions, weeding out dangerous and wrong ideas. This requires creating knowledge graphs containing negative information: the information of what cannot happen or should not be done and why. The problems of creating negative knowledge graphs are discussed. Hybrid cognitive systems using the proposed architecture will be a lot more trustworthy as they will have a system of human-verifiable rules that ensures avoiding the worst errors which can be used in many fields from decision making to natural-language parsing. © 2020 Elsevier B.V.. All rights reserved.},
correspondence_address1={Sychev, O.; Volgograd State Technical University, Lenin Ave, 28, Russian Federation; эл. почта: oasychev@gmail.com},
publisher={Elsevier B.V.},
issn={18770509},
language={English},
abbrev_source_title={Procedia Comput. Sci.},
thanks = {rfbr-20-07-00764}
}

@InProceedings{10.1007/978-3-030-80421-3_6,
author="Sychev, Oleg
and Anikin, Anton
and Penskoy, Nikita
and Denisov, Mikhail
and Prokudin, Artem",
editor="Cristea, Alexandra I.
and Troussas, Christos",
title="CompPrehension - Model-Based Intelligent Tutoring System on Comprehension Level",
booktitle="Intelligent Tutoring Systems",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="52--59",
abstract="Intelligent tutoring systems become increasingly common in assisting human learners, but they are often aimed at isolated domain tasks without creating a scaffolding system from lower- to higher-level cognitive skills. We designed and implemented an intelligent tutoring system CompPrehension aimed at the comprehension level of Bloom's taxonomy that often gets neglected in favour of the higher levels. The system features plugin-based architecture, easing adding new domains and learning strategies; using formal models and software reasoners to solve the problems and judge the answers; and generating explanatory feedback and follow-up questions to stimulate the learners' thinking. The architecture and workflow are shown. We demonstrate the process of interacting with the system in the Control Flow Statements domain. The advantages and limits of the developed system are discussed.",
isbn="978-3-030-80421-3",
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Sychev2021mdpi,
author={Oleg Sychev and Nikita Penskoy and Anton Anikin and Mikhail Denisov and Artem Prokudin},
title={Improving comprehension: Intelligent tutoring system explaining the domain rules when students break them},
journal={Education Sciences},
year={2021},
volume={11},
number={11},
doi={10.3390/educsci11110719},
art_number={719},
url={https://www.mdpi.com/2227-7102/11/11/719},
affiliation={Software Engineering Department, Electronics and Computing Machinery Faculty, Volgograd State Technical University, Volgograd, 400005, Russian Federation},
abstract={Intelligent tutoring systems have become increasingly common in assisting students but are often aimed at isolated subject-domain tasks without creating a scaffolding system from lower-to higher-level cognitive skills, with low-level skills often neglected. We designed and developed an intelligent tutoring system, CompPrehension, which aims to improve the comprehension level of Bloom’s taxonomy. The system features plug-in-based architecture, easily adding new subject domains and learning strategies. It uses formal models and software reasoners to solve the problems and judge the answers, and generates explanatory feedback about the broken domain rules and follow-up questions to stimulate the students’ thinking. We developed two subject domain models: an Expressions domain for teaching the expression order of evaluation, and a Control Flow Statements domain for code-tracing tasks. The chief novelty of our research is that the developed models are capable of automatic problem classification, determining the knowledge required to solve them and so the pedagogical conditions to use the problem without human participation. More than 100 undergraduate first-year Computer Science students took part in evaluating the system. The results in both subject domains show medium but statistically significant learning gains after using the system for a few days; students with worse previous knowledge gained more. In the Control Flow Statements domain, the number of completed questions correlates positively with the post-test grades and learning gains. The students’ survey showed a slightly positive perception of the system. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
correspondence_address1={Sychev, O.; Software Engineering Department, Russian Federation; эл. почта: o_sychev@vstu.ru; Anikin, A.; Software Engineering Department, Russian Federation; эл. почта: anton.anikin@vstu.ru},
publisher={MDPI},
issn={22277102},
language={English},
abbrev_source_title={Educ. Sci.},
thanks = {rfbr-20-07-00764}
}

@CONFERENCE{Sychev2021_2,
author={Sychev, O. and Anikin, A. and Terekhov, G.},
title={Demonstrating Concepts through Visual Simulators: Two Cases in the Programming Domain},
journal={Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC},
year={2021},
volume={2010-October},
doi={10.1109/VL/HCC51201.2021.9576398},
url={https://ieeexplore.ieee.org/document/9576398},
affiliation={Volgograd State Technical University, Software Engineering Department, Volgograd, Russian Federation},
abstract={One of the ways to demonstrate subject-domain concepts is to let the learner play in the domain-related sandbox, while receiving the explanations of the domain laws that were broken when the user makes a wrong move. This can help the learners who understand definitions of the concepts poorly. We present two visual simulators for demonstrating concept properties and domain laws, implemented as web applications. They can be used to study programming, enabling trial-and-error learning, supported by the error messages, explaining why the user's action was wrong. © 2021 IEEE.},
editor={Harms K., Cunha J., Oney S., Kelleher C.},
publisher={IEEE Computer Society},
issn={19436092},
isbn={9781665445924},
language={English},
abbrev_source_title={Proc. of IEEE Symp. Vis. Lang. Hum.-Cent. Comput., VL/HCC},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Anikin2021_3,
author={Anikin, A. and Sychev, O. and Denisov, M.},
title={Ontology reasoning for explanatory feedback generation to teach how algorithms work},
journal={Frontiers in Artificial Intelligence and Applications},
year={2021},
volume={338},
pages={V-VI},
doi={10.3233/FAIA210100},
art_number={239-244},
url={https://ebooks.iospress.nl/doi/10.3233/FAIA210100},
affiliation={Volgograd State Technical University, Russian Federation; Software Engineering School, Volgograd, Russian Federation},
abstract={Developing algorithms using control structures and understanding their building blocks are essential skills in mastering programming. Ontologies and software reasoning is a promising method for developing intelligent tutoring systems in well-defined domains (like programming languages and algorithms); it can be used for many kinds of teaching tasks. In this work, we used a formal model consisting of production rules for Apache Jena reasoner as a basis for developing a constraint-based tutor for introductory programming domain. The tutor can determine fault reasons for any incorrect answer that a student can enter. The problem the student should solve is building an execution trace for the given algorithm. The problem is a closed-ended question that requires arranging given actions in the (unique) correct order; some actions can be used several times, while others can be omitted. Using formal reasoning to check domain constraints allowed us to provide explanatory feedback for all kinds of errors students can make. © 2021 The authors and IOS Press.},
editor={Frasson C., Kabassi K., Voulodimos A.},
publisher={IOS Press BV},
issn={09226389},
isbn={9781643682044},
language={English},
abbrev_source_title={Front. Artif. Intell. Appl.},
thanks = {rfbr-20-07-00764}
}

@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}
}

@ARTICLE{Sychev2021501,
author={Sychev, O. and Kamennov, Y.},
title={Eligibility of English Hypernymy Resources for Extracting Knowledge from Natural-Language Texts},
journal={Advances in Intelligent Systems and Computing},
year={2021},
volume={1310},
pages={501-507},
doi={10.1007/978-3-030-65596-9_61},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-65596-9_61},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
abstract={A common subtask of knowledge acquisition from natural-language texts is classifying words and recognizing entities and actions in the text. It is used in the analysis of both scientific and narrative texts. Thesauri and lexical databases containing hypernymy relationship between synsets may be a useful resource for entity and action recognition. In this study, we compared the performance of three major English thesauri containing hypernymy relationship in different forms - WordNet, Roget’s Thesaurus, and FrameNet - on 6 word-meaning categories that are used for the analysis of narrative and scientific natural-language texts. The results show that WordNet contains more words than FrameNet, and is more suitable for scientific texts, but FrameNet contains better-defined hypernyms and shows better precision for many narrative natural-language tasks, especially for verbs. Roget’s Thesaurus performance is average between WordNet and FrameNet in most word-meaning categories Enhancing FrameNet by adding more lexical units to existing frames would allow creating a powerful resource for entity and action recognition in text analysis. Fixing WordNet problems require revising its system of hypernyms. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
correspondence_address1={Sychev, O.; Volgograd State Technical UniversityRussian Federation; эл. почта: oasychev@gmail.com},
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}
}

@CONFERENCE{Sychev2021623,
author={Sychev, O.},
title={CorrectWriting: Open-Ended Question with Hints for Teaching Programming-Language Syntax},
journal={Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE},
year={2021},
pages={623-624},
doi={10.1145/3456565.3460030},
url={https://dl.acm.org/doi/10.1145/3456565.3460030},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
abstract={Teaching students to construct correct statements in programming languages is an important part of introductory programming courses. While modern development environments highlight syntax errors, they do not stimulate thinking in grammatical terms to help the student understand and memorize syntax rules. To facilitate learning syntax, we developed CorrectWriting, a question-type plug-in for the popular LMS Moodle. It finds mistakes in token order and composition and detects typos, including missing and extraneous separators. Mistake messages use teacher-supplied token descriptions to show the grammatical role of each wrong token. Hints are provided about students' mistakes. CorrectWriting questions are actively used by the students of Volgograd State Technical University to prepare for classwork. © 2021 Owner/Author.},
correspondence_address1={Sychev, O.; Volgograd State Technical UniversityRussian Federation; эл. почта: oasychev@gmail.com},
publisher={Association for Computing Machinery},
issn={1942647X},
isbn={9781450383974},
language={English},
abbrev_source_title={Annu. Conf. Innov. Technol. Comput. Sci. Educ. ITiCSE},
thanks = {rfbr-20-07-00764}
}

@CONFERENCE{Sychev2021_6,
author={Sychev, O.A. and Prokudin, A.A. and Evtushenko, O.E. and Toporkova, O.V.},
title={The impact of formative quizzes using CorrectWriting question type on learning word order in an ESL course},
journal={Journal of Physics: Conference Series},
year={2021},
volume={1801},
number={1},
doi={10.1088/1742-6596/1801/1/012011},
art_number={012011},
url={https://iopscience.iop.org/article/10.1088/1742-6596/1801/1/012011},
affiliation={Volgograd State Technical University, Lenina Avenue 28, Volgograd, 400005, Russian Federation},
abstract={Studying word order in English sentences is a problem for ESL students whose native languages allow flexible word orders. Developing skills in formulating English sentences often requires trial-and-error process that takes time and requires supervision. Quiz software that is able to analyse students' answers, and give meaningful feedback on mistakes in word order and hints on fixing them allows training without teacher's supervision that greatly enhances the amount of attempts a student can do. In this study, we used CorrectWriting question type for Moodle LMS to create formative and summative quizzes for Russian students learning English as a second language. The experiments show that the students who made more than one attempt of formative quizzes had significantly better learning gains than the students who didn't use formative quizzes or attempted them only once. It also showed vulnerabilities in question design that need to be fixed to utilise software features better in learning process. © Published under licence by IOP Publishing Ltd.},
correspondence_address1={Sychev, O.A.; Volgograd State Technical University, Lenina Avenue 28, Russian Federation; эл. почта: oasychev@gmail.com},
publisher={IOP Publishing Ltd},
issn={17426588},
language={English},
abbrev_source_title={J. Phys. Conf. Ser.},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Sychev2021404,
author={Sychev, O.},
title={Visualizing Program State as a Clustered Graph for Learning Programming},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2021},
volume={12909 LNAI},
pages={404-407},
doi={10.1007/978-3-030-86062-2_41},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-86062-2_41},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
abstract={The poster presents a method of visualizing the program state as a diagram of data elements that can both include nested elements and reference other elements. It differs from the known methods because it allows nested objects and arrays; the method also acknowledges high-level data structures like containers and iterators. Layout methods for graphs of this complexity are discussed, and examples of the resulting graphs are provided. The approach was used in computer science courses; the survey showed that it was popular among middle- and low-performing students while high-performing students preferred a professional tool, giving more compact images. Educational uses of the generated diagrams are discussed. © 2021, Springer Nature Switzerland AG.},
correspondence_address1={Sychev, O.; Volgograd State Technical UniversityRussian Federation; эл. почта: o_sychev@vstu.ru},
editor={Basu A., Stapleton G., Linker S., Legg C., Manalo E., Viana P.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={03029743},
isbn={9783030860615},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Denisov2021408,
author={Denisov, M. and Anikin, A. and Sychev, O.},
title={Dynamic Flowcharts for Enhancing Learners’ Understanding of the Control Flow During Programming Learning},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2021},
volume={12909 LNAI},
pages={408-411},
doi={10.1007/978-3-030-86062-2_42},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-86062-2_42},
affiliation={Volgograd State Technical University, Lenin Ave, 28, Volgograd, 400005, Russian Federation},
abstract={In introductory programming learning, flowcharts are often used to help students comprehend patterns of behavior of control flow statements like alternatives, loops, and switches. The use of flowcharts in assignments along with lectures can increase their learning effect. In some areas, the potential of flowcharts remains largely underestimated. Flowcharts are rare in intelligent learning systems for learning programming, although class, object, and sequence diagrams are quite popular. This poster describes the implementation of a non-editable flowchart widget in our intelligent programming tutor on control flow structures (hereinafter referred to as the exerciser). In normal use, the student chooses the next correct action in the program text (algorithm), i.e., makes one transition through the flowchart. Our exerciser allows making a wrong choice and gives a text hint explaining the reason for incorrectness. The wrong or inexistent transition is also displayed on the flowchart to help localize the error. © 2021, Springer Nature Switzerland AG.},
correspondence_address1={Anikin, A.; Volgograd State Technical University, Lenin Ave, 28, Russian Federation; эл. почта: anton.anikin@vstu.ru},
editor={Basu A., Stapleton G., Linker S., Legg C., Manalo E., Viana P.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={03029743},
isbn={9783030860615},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Uglev2021443,
author={Uglev, V. and Sychev, O.},
title={Concentrating Competency Profile Data into Cognitive Map of Knowledge Diagnosis},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2021},
volume={12909 LNAI},
pages={443-446},
doi={10.1007/978-3-030-86062-2_46},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-86062-2_46},
affiliation={Siberian Federal University, Zheleznogorsk, Russian Federation; Volgograd State Technical University, Volgograd, Russian Federation},
abstract={The paper describes the process of aggregating primary learning data in the form of learning digital footprints for managing educational process using cognitive visualization techniques. The over-arching process of transition from the learning data to competency diagrams and concentrate them into Cognitive Maps of Cnowledge Diagnosis that. The competency profile, represented as a radar-chart diagram, is compressed into a cognitive map, allowing interpreting this information during decision making by faculty and administrative staff. This allows generalizing results for groups of students. The results of using competency profiles and respective cognitive maps to analyze the results of summative assessments, final, and cross-curricular exams are provided as an illustration of the proposed approach. © 2021, Springer Nature Switzerland AG.},
correspondence_address1={Uglev, V.; Siberian Federal UniversityRussian Federation; эл. почта: vauglev@sfu-kras.ru},
editor={Basu A., Stapleton G., Linker S., Legg C., Manalo E., Viana P.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={03029743},
isbn={9783030860615},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Novozhenina2021681,
author={Novozhenina, E. and Sychev, O. and Toporkova, O. and Evtushenko, O.},
title={Teaching English Word Order with CorrectWriting Software},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2021},
volume={12951 LNCS},
pages={681-692},
doi={10.1007/978-3-030-86970-0_47},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-86970-0_47},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
abstract={The article looks into the problem of using interactive computer technologies in technology-enhanced teaching English at a technical university. An electronic course “English Word Order” using CorrectWriting software for Moodle LMS was developed and used to teach students of various levels of education. The system of formative and summative assessments regarding the word order of English sentences was studied. The students’ responses to CorrectWriting questions were analyzed using the Longest Common Subsequence algorithm, allowing determining misplaced, missing and extraneous words. During formative assessments, the students were offered automatic hints on how to fix their mistakes without having to wait for feedback from the teacher. The learning gains and the student survey prove the efficiency of the developed online course which helps to improve students’ grammar skills during the classes and unsupervised work. The study also notes a positive role of electronic teaching materials in optimizing the technological and organizational support of the educational process. Further work on enhancing the developed course and software is outlined. © 2021, Springer Nature Switzerland AG.},
correspondence_address1={Sychev, O.; Volgograd State Technical UniversityRussian Federation; эл. почта: o_sychev@vstu.ru},
editor={Gervasi O., Murgante B., Misra S., Garau C., Blecic I., Taniar D., Apduhan B.O., Rocha A.M., Tarantino E., Torre C.M.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={03029743},
isbn={9783030869694},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
thanks = {rfbr-20-07-00764}
}

@ARTICLE{Uglev202193,
author={Uglev, V. and Sychev, O.},
title={Creating and Visualising Cognitive Maps of Knowledge Diagnosis During the Processing of Learning Digital Footprint},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2021},
volume={12677 LNCS},
pages={93-98},
doi={10.1007/978-3-030-80421-3_11},
url={https://link.springer.com/chapter/10.1007%2F978-3-030-86970-0_47},
affiliation={Siberian Federal University, Zheleznogorsk, Russian Federation; Volgograd State Technical University, Volgograd, Russian Federation},
abstract={The paper describes the problem of creating a single mapping of the learning situation during the processing of digital footprint and decision making in intelligent tutoring systems. We propose creating a Cognitive Map of Knowledge Diagnosis as a way of summarising data in the digital footprint. The elements of this cognitive map and the details of their visualisation are described. The results of the use of cognitive maps are demonstrated on the example of personalising the content of an online learning course and providing detailed feedback. © 2021, Springer Nature Switzerland AG.},
correspondence_address1={Uglev, V.; Siberian Federal UniversityRussian Federation; эл. почта: uglev-v@yandex.ru},
editor={Cristea A.I., Troussas C.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={03029743},
isbn={9783030804206},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
thanks = {rfbr-20-07-00764}
}

@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}
}

@CONFERENCE{Sychev2021621,
author={Sychev, O. and Denisov, M. and Terekhov, G.},
title={How it Works: Algorithms-A Tool for Developing an Understanding of Control Structures},
journal={Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE},
year={2021},
pages={621-622},
doi={10.1145/3456565.3460032},
url={https://dl.acm.org/doi/10.1145/3456565.3460032},
affiliation={Volgograd State Technical University, Volgograd, Russian Federation},
abstract={Developing an understanding of control structures is one of the important tasks in introductory programming courses. To facilitate active learning with immediate feedback, we developed a constraint-based tutor, "How it Works: Algorithms,"that asks students to build an execution trace of the given algorithm and provides explanatory feedback about the mistakes the student made. The reasons for the student's faults are determined by an inference engine, using a set of rules describing the subject domain. Teachers can create exercises using a simple visual block-based interface and sending them to students as permanent links. © 2021 Owner/Author.},
publisher={Association for Computing Machinery},
issn={1942647X},
isbn={9781450383974},
language={English},
abbrev_source_title={Annu. Conf. Innov. Technol. Comput. Sci. Educ. ITiCSE},
thanks = {rfbr-20-07-00764}
}

@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"
}