Heterogeneity Reduction for Data Refining Within Ontology Learning Process
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00330525" target="_blank" >RIV/68407700:21730/18:00330525 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8592821" target="_blank" >https://ieeexplore.ieee.org/document/8592821</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IECON.2018.8592821" target="_blank" >10.1109/IECON.2018.8592821</a>
Alternative languages
Result language
angličtina
Original language name
Heterogeneity Reduction for Data Refining Within Ontology Learning Process
Original language description
The (semi-)automated integration of new information into a data model is a functionality which is required in cases when input documents are extensive and therefore a manual integration difficult or even impossible. We proposed the solution combining the ontology learning process with information acquisition from the Web (web mining). This approach offers a robust way how to integrate even previously unknown information disregarding target application or domain. The solution deals with facilitating identification of input data among existing concepts or with the definition of a new concept. The proposed solution was experimentally verified on the integration of an excel document containing spare parts and Ford Supply Chain Ontology.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
ISBN
978-1-5090-6685-8
ISSN
1553-572X
e-ISSN
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Number of pages
6
Pages from-to
3108-3113
Publisher name
American Institute of Physics and Magnetic Society of the IEEE
Place of publication
San Francisco
Event location
Washington D.C.
Event date
Oct 21, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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