Domain-Oriented Multilevel Ontology for Adaptive Data Processing

Man Tianxing, Elena Stankova, Alexander Vodyaho, Nataly Zhukova, Yulia Shichkina

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2020
Subtitle of host publication20th International Conference, Cagliari, Italy, July 1–4, 2020, Proceedings, Part I
EditorsOsvaldo Gervasi, et al.
PublisherSpringer Nature
Pages634-649
ISBN (Print)9783030587987
DOIs
StatePublished - 2020
Event20th International Conference on Computational Science and Its Applications, ICCSA 2020 - Cagliari, Italy
Duration: 1 Jul 20204 Jul 2020
http://iccsa.org/

Publication series

NameLecture Notes in Computer Science
Volume12249
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Computational Science and Its Applications, ICCSA 2020
Abbreviated titleICCSA 2020
Country/TerritoryItaly
CityCagliari
Period1/07/204/07/20
Internet address

Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Data mining
  • Meta-learning
  • Ontology
  • Semantic meta mining

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