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bibtex [2019/03/22 13:51]
anton
bibtex [2019/09/29 09:20]
anton
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 +@article{Sychev2019,​
 +  doi = {10.1016/​j.cogsys.2019.09.025},​
 +  url = {https://​doi.org/​10.1016/​j.cogsys.2019.09.025},​
 +  year = {2019},
 +  month = sep,
 +  publisher = {Elsevier {BV}},
 +  author = {Oleg Sychev and Anton Anikin and Artem Prokudin},
 +  title = {Automatic grading and hinting in open-ended text questions},
 +  journal = {Cognitive Systems Research},
 +  thanks = {rfbr-18-07-00032}
 +}
 +
 +@InProceedings{10.1007/​978-3-030-29750-3_33,​
 +doi="​10.1007/​978-3-030-29750-3_33",​
 +author="​Kultsova,​ Marina
 +and Potseluico, Anastasiya
 +and Dvoryankin, Alexander",​
 +editor="​Kravets,​ Alla G.
 +and Groumpos, Peter P.
 +and Shcherbakov,​ Maxim
 +and Kultsova, Marina",​
 +title="​Ontology Based Personalization of Mobile Interfaces for People with Special Needs",​
 +booktitle="​Creativity in Intelligent Technologies and Data Science",​
 +year="​2019",​
 +publisher="​Springer International Publishing",​
 +address="​Cham",​
 +pages="​422--433",​
 +abstract="​The paper is devoted to a problem of interface personification for people with special needs on the base of information about their behavior during interaction with mobile applications. This work evolves our previous researches on the development of adaptive user interfaces. Much attention in this paper was given to the investigation of existing approaches to collecting and analyzing the information about user behavior and interaction context data as well as interface adaptation recommendations. The improved interface adaptation mechanism was developed and described based on the ontological representation of the interface patterns and knowledge about users and their interaction with a mobile application. The set of adaptation rules was developed and implemented in the ontology knowledge base. In the paper, we described the improved ontology model and some examples of ontological representation of interface patterns.",​
 +isbn="​978-3-030-29750-3",​
 +thanks = {rfbr-18-07-00032}
 +}
 +
 +
 +@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}
 +}
 +
 +
 +@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}
 +}
 +@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}
 +}
 @article{Anikin_2019_1,​ @article{Anikin_2019_1,​
  doi = {10.1088/​1757-899x/​483/​1/​012074},​  doi = {10.1088/​1757-899x/​483/​1/​012074},​
Line 10: Line 86:
  title = {Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research},  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},​  journal = {{IOP} Conference Series: Materials Science and Engineering},​
- thanks = {18-07-00032,​ 18-47-340014},​+ 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.}
 } }
Line 25: Line 101:
  title = {Using online update of distributional semantics models for decision-making support for concepts extraction in the domain ontology learning task},  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},​  journal = {{IOP} Conference Series: Materials Science and Engineering},​
- thanks = {18-07-00032},​+ 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.}
 } }