RFBR grant 18-47-340014 (2018-2019): Development of the mechanism of semantic zooming for the ontology geometric OWL graph to increase the efficiency of decision making in the tasks of learning a new domain, storage and sharing knowledge
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 (2)
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.