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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3N36AHP
Repositorysid.inpe.br/mtc-m21b/2016/12.20.16.11
Last Update2021:02.12.13.21.20 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21b/2016/12.20.16.11.52
Metadata Last Update2023:08.16.17.49.16 (UTC) administrator
Secondary KeyINPE--PRE/
Citation KeyBarchiHrusCostCarv:2016:GaOnEx
TitleGalaxies ontology extension through deep learning
Year2016
Access Date2024, May 16
Secondary TypePRE CN
Number of Files1
Size1004 KiB
2. Context
Author1 Barchi, Paulo
2 Hruschka Junior, Estevam
3 Costa, Fausto Guzzo da
4 Carvalho, Reinaldo Ramos de
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JJ5B
Group1
2
3
4 DAS-CEA-INPE-MCTI-GOV-BR
Affiliation1
2
3
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3
4 reinaldo@das.inpe.br
Conference NameWorkshop de Computação Aplicada, 16 (WORCAP)
Conference LocationSão José dos Campos, SP
Date25-26 out.
History (UTC)2016-12-20 16:12:05 :: simone -> administrator :: 2016
2018-06-04 02:41:42 :: administrator -> simone :: 2016
2021-02-12 13:21:21 :: simone -> administrator :: 2016
2023-08-16 17:49:16 :: administrator -> simone :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
AbstractOntology Extension is a Machine Learning (ML) technique commonly used to expand Knowledge Bases (KB). One approach in Machine Reading (MR) is to identify and add to the KB new relations that are frequently asserted in huge text data. These huge amount of data (not necessarily text) can also be referred as data hypercube because of its simple data structure and logic which represent the data in the (multiple) dimensions of interest. Co-occurrence matrices are used to structure the normalized values of co-occurrence between the contexts for each category pair to identify those context patterns. After the clustering phase, from each cluster arises a new possible relation. This work presents a new application to use this approach to expand the Ontology of Galaxies. Convolution Neural Networks (CNN) are deep neural networks appropriate to handle images. The main idea is to train, test and validate one CNN connected to a Support Vector Machine (SVM) - well known for its strong theoretical base and practical effiency - from Galaxy Zoo data warehousing; and apply this system (CNN with SVM) to classify new galaxies never seen before from the SLOAN database, and thus, to extend the ontology of galaxies.
AreaCEA
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDAS > Galaxies ontology extension...
Arrangement 2Galaxies ontology extension...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 20/12/2016 14:11 1.0 KiB 
4. Conditions of access and use
data URLhttp://mtc-m21b.sid.inpe.br/ibi/8JMKD3MGP3W34P/3N36AHP
zipped data URLhttp://mtc-m21b.sid.inpe.br/zip/8JMKD3MGP3W34P/3N36AHP
Languageen
Target Filebarchi_galaxies.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/3ETR8EH
8JMKD3MGPDW34P/49L898E
Citing Item Listsid.inpe.br/mtc-m16c/2023/08.16.17.44 2
sid.inpe.br/mtc-m21/2012/07.13.14.58.48 1
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
Empty Fieldsarchivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readpermission rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url versiontype volume
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