Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Domain-Oriented Multilevel Ontology for Adaptive Data Processing. / Tianxing, Man; Stankova, Elena; Vodyaho, Alexander; Zhukova, Nataly; Shichkina, Yulia.
Computational Science and Its Applications – ICCSA 2020 : 20th International Conference, Cagliari, Italy, July 1–4, 2020, Proceedings, Part I. ред. / Osvaldo Gervasi; et al. Springer Nature, 2020. стр. 634-649 (Lecture Notes in Computer Science ; Том 12249 ).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Domain-Oriented Multilevel Ontology for Adaptive Data Processing
AU - Tianxing, Man
AU - Stankova, Elena
AU - Vodyaho, Alexander
AU - Zhukova, Nataly
AU - Shichkina, Yulia
N1 - Tianxing M., Stankova E., Vodyaho A., Zhukova N., Shichkina Y. (2020) Domain-Oriented Multilevel Ontology for Adaptive Data Processing. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_46
PY - 2020
Y1 - 2020
N2 - In the data mining domain, the diversity of algorithms and the clutter of data make the knowledge discovery process very unfriendly to many non-computer professional researchers. Meta-learning helps users to modify some aspects of this process to improve the performance of the resulting model. Semantic meta mining is the process of mining metadata about data mining algorithms based on expertise extracted from the knowledge base. The knowledge base is usually represented in the form of ontology. This article proposes a domain-oriented multi-level ontology (DoMO) through merging and improving existing data mining ontologies. It provides the restrictions of the dataset characteristics to help the domain experts describe data set in the form of ontology entities. According to the entities of the data characteristics in DoMO, the users can query the ontology to obtain the optimized data processing process. In this paper, we take the time series classification problem as an example to present the effectiveness of the proposed ontology.
AB - In the data mining domain, the diversity of algorithms and the clutter of data make the knowledge discovery process very unfriendly to many non-computer professional researchers. Meta-learning helps users to modify some aspects of this process to improve the performance of the resulting model. Semantic meta mining is the process of mining metadata about data mining algorithms based on expertise extracted from the knowledge base. The knowledge base is usually represented in the form of ontology. This article proposes a domain-oriented multi-level ontology (DoMO) through merging and improving existing data mining ontologies. It provides the restrictions of the dataset characteristics to help the domain experts describe data set in the form of ontology entities. According to the entities of the data characteristics in DoMO, the users can query the ontology to obtain the optimized data processing process. In this paper, we take the time series classification problem as an example to present the effectiveness of the proposed ontology.
KW - Data mining
KW - Meta-learning
KW - Ontology
KW - Semantic meta mining
UR - http://www.scopus.com/inward/record.url?scp=85092723602&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/fa4101fe-2d39-3c3f-b82a-0b05f18a6208/
U2 - 10.1007/978-3-030-58799-4_46
DO - 10.1007/978-3-030-58799-4_46
M3 - Conference contribution
AN - SCOPUS:85092723602
SN - 9783030587987
T3 - Lecture Notes in Computer Science
SP - 634
EP - 649
BT - Computational Science and Its Applications – ICCSA 2020
A2 - Gervasi, Osvaldo
A2 - null, et al.
PB - Springer Nature
T2 - 20th International Conference on Computational Science and Its Applications, ICCSA 2020
Y2 - 1 July 2020 through 4 July 2020
ER -
ID: 70310902