Classifying the LOD cloud: Digging into the knowledge graph
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Information Science |
Texto Completo: | https://revistas.marilia.unesp.br/index.php/bjis/article/view/8328 |
Resumo: | Massive amounts of data from different contexts and producers are collected and connected relying often solely on statistical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminology employed within the LOD cloud with terminology employed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in computer science, and the KO community, with members from linguistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communities. |
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Brazilian Journal of Information Science |
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Classifying the LOD cloud: Digging into the knowledge graphLinked Open DataKnowledge Organisation SystemsBig DataKnowledge GraphMassive amounts of data from different contexts and producers are collected and connected relying often solely on statistical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminology employed within the LOD cloud with terminology employed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in computer science, and the KO community, with members from linguistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communities.Faculdade de Filosofia e Ciências2018-12-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.marilia.unesp.br/index.php/bjis/article/view/832810.36311/1981-1640.2018.v12n4.02.p6Brazilian Journal of Information Science: Research Trends; Vol. 12 No. 4 (2018); 06-10Brazilian Journal of Information Science: Research Trends; Vol. 12 Núm. 4 (2018); 06-10Brazilian Journal of Information Science: research trends; v. 12 n. 4 (2018); 06-101981-1640reponame:Brazilian Journal of Information Scienceinstname:Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP)instacron:UNESPenghttps://revistas.marilia.unesp.br/index.php/bjis/article/view/8328/5415Copyright (c) 2018 Daniel Martínez Ávila, Richard P. Smiraglia, Rick Szostak, Andrea Scharnhorst, Wouter Beek, Ronald Siebes, Laura Ridenour, Vanessa Schlaishttps://creativecommons.org/licenses/by-sa/4.0info:eu-repo/semantics/openAccessMartínez Ávila, DanielSmiraglia, Richard P.Szostak, RickScharnhorst, AndreaBeek, WouterSiebes, RonaldRidenour, LauraSchlais, Vanessa2022-12-22T12:29:09Zoai:ojs.www2.marilia.unesp.br:article/8328Revistahttps://revistas.marilia.unesp.br/index.php/bjis/indexPUBhttps://revistas.marilia.unesp.br/index.php/bjis/oaibrajis.marilia@unesp.br||1981-16401981-1640opendoar:2022-12-22T12:29:09Brazilian Journal of Information Science - Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP)false |
dc.title.none.fl_str_mv |
Classifying the LOD cloud: Digging into the knowledge graph |
title |
Classifying the LOD cloud: Digging into the knowledge graph |
spellingShingle |
Classifying the LOD cloud: Digging into the knowledge graph Martínez Ávila, Daniel Linked Open Data Knowledge Organisation Systems Big Data Knowledge Graph |
title_short |
Classifying the LOD cloud: Digging into the knowledge graph |
title_full |
Classifying the LOD cloud: Digging into the knowledge graph |
title_fullStr |
Classifying the LOD cloud: Digging into the knowledge graph |
title_full_unstemmed |
Classifying the LOD cloud: Digging into the knowledge graph |
title_sort |
Classifying the LOD cloud: Digging into the knowledge graph |
author |
Martínez Ávila, Daniel |
author_facet |
Martínez Ávila, Daniel Smiraglia, Richard P. Szostak, Rick Scharnhorst, Andrea Beek, Wouter Siebes, Ronald Ridenour, Laura Schlais, Vanessa |
author_role |
author |
author2 |
Smiraglia, Richard P. Szostak, Rick Scharnhorst, Andrea Beek, Wouter Siebes, Ronald Ridenour, Laura Schlais, Vanessa |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Martínez Ávila, Daniel Smiraglia, Richard P. Szostak, Rick Scharnhorst, Andrea Beek, Wouter Siebes, Ronald Ridenour, Laura Schlais, Vanessa |
dc.subject.por.fl_str_mv |
Linked Open Data Knowledge Organisation Systems Big Data Knowledge Graph |
topic |
Linked Open Data Knowledge Organisation Systems Big Data Knowledge Graph |
description |
Massive amounts of data from different contexts and producers are collected and connected relying often solely on statistical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminology employed within the LOD cloud with terminology employed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in computer science, and the KO community, with members from linguistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communities. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-12 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.marilia.unesp.br/index.php/bjis/article/view/8328 10.36311/1981-1640.2018.v12n4.02.p6 |
url |
https://revistas.marilia.unesp.br/index.php/bjis/article/view/8328 |
identifier_str_mv |
10.36311/1981-1640.2018.v12n4.02.p6 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistas.marilia.unesp.br/index.php/bjis/article/view/8328/5415 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by-sa/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-sa/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Faculdade de Filosofia e Ciências |
publisher.none.fl_str_mv |
Faculdade de Filosofia e Ciências |
dc.source.none.fl_str_mv |
Brazilian Journal of Information Science: Research Trends; Vol. 12 No. 4 (2018); 06-10 Brazilian Journal of Information Science: Research Trends; Vol. 12 Núm. 4 (2018); 06-10 Brazilian Journal of Information Science: research trends; v. 12 n. 4 (2018); 06-10 1981-1640 reponame:Brazilian Journal of Information Science instname:Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Brazilian Journal of Information Science |
collection |
Brazilian Journal of Information Science |
repository.name.fl_str_mv |
Brazilian Journal of Information Science - Universidade Estadual Paulista Júlio de Mesquita Filho (UNESP) |
repository.mail.fl_str_mv |
brajis.marilia@unesp.br|| |
_version_ |
1754840471693164544 |