Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/9490 |
Resumo: | Large growing knowledge bases have been an interesting field in many researches in the past few years. Most techniques focus on constructing algorithms to help a Knowledge Base (KB) automatically (or semi automatically) expands. However, many tools used to expand the KBs can extract incomplete or incorrect data, turning the KB inconsistent. In this way, this work has the objective to expand large knowledge bases as well as detect inconsistencies on them. To accomplish that, an association rule mining algorithm and temporal correlation are used. Applying an algorithm to extract association rules in large knowledge bases, the missing value problem need to be considered, once these bases grow day to day, and do not have all of the data. Therefore, a new parameter was created to perform the support calculation, the MSC parameter, to deal with missing values. Besides, a major problem on using association rules is the effort spent to analyze each extracted rule. Thus, this work developed ER component, which eliminates redundant and irrelevant association rules. Each valid rule is used by TARE component with the purpose of detecting inconsistencies. TARE introduces the concept of STARs (specific temporal association rules), which are used to detect possible inconsistencies. Each relevant STAR is used as an input to TCI component in order to get temporal correlations to (i) detect possible inconsistencies and (ii) to help populating the KB. Experiments showed that the association rules and the temporal correlation are capable to expand the knowledge base, decreasing the amount of missing values. Moreover, both TARE and TCI components were efficient in the process of detecting possible inconsistencies in the data set. Finally, the ER component reduced the number of rules in more then 30% without any lost in the process of populating the KB. |
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Miani, Rafael Garcia LeonelHruschka Júnior, Estevam Rafaelhttp://lattes.cnpq.br/2097340857065853http://lattes.cnpq.br/94872350965983555530e5cc-cbf4-4ceb-8835-399dc418545f2018-02-27T19:55:50Z2018-02-27T19:55:50Z2017-12-20MIANI, Rafael Garcia Leonel. Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento. 2017. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9490.https://repositorio.ufscar.br/handle/ufscar/9490Large growing knowledge bases have been an interesting field in many researches in the past few years. Most techniques focus on constructing algorithms to help a Knowledge Base (KB) automatically (or semi automatically) expands. However, many tools used to expand the KBs can extract incomplete or incorrect data, turning the KB inconsistent. In this way, this work has the objective to expand large knowledge bases as well as detect inconsistencies on them. To accomplish that, an association rule mining algorithm and temporal correlation are used. Applying an algorithm to extract association rules in large knowledge bases, the missing value problem need to be considered, once these bases grow day to day, and do not have all of the data. Therefore, a new parameter was created to perform the support calculation, the MSC parameter, to deal with missing values. Besides, a major problem on using association rules is the effort spent to analyze each extracted rule. Thus, this work developed ER component, which eliminates redundant and irrelevant association rules. Each valid rule is used by TARE component with the purpose of detecting inconsistencies. TARE introduces the concept of STARs (specific temporal association rules), which are used to detect possible inconsistencies. Each relevant STAR is used as an input to TCI component in order to get temporal correlations to (i) detect possible inconsistencies and (ii) to help populating the KB. Experiments showed that the association rules and the temporal correlation are capable to expand the knowledge base, decreasing the amount of missing values. Moreover, both TARE and TCI components were efficient in the process of detecting possible inconsistencies in the data set. Finally, the ER component reduced the number of rules in more then 30% without any lost in the process of populating the KB.Grandes bases de conhecimento crescente têm sido um interessante campo em muitas pesquisas nos últimos anos. A maioria das técnicas focam na construção de algoritmos para auxiliar a Base de Conhecimento (BC) a expandir automaticamente (ou semiautomaticamente). Entretanto, muitas ferramentas utilizadas para a expandir as BCs podem extrair dados incompletos ou incorretos, tornando a base inconsistente. Dessa forma, este trabalho possui o objetivo de expandir as grandes bases de conhecimento e detectar inconsistências nas mesmas. Para tal, são utilizadas a mineração de regras de associação e a correlação temporal. Ao aplicar um algoritmo de extração de regras de associação em grandes bases de conhecimento, é necessário considerar o problema de valores ausentes, uma vez que elas crescem diariamente, não possuindo todos os dados. Logo, foi criado um novo parâmetro para realizar o cálculo do suporte, denominado MSC, para trabalhar com valores ausentes. Além disso, um grande problema ao utilizar regras de associação é o esforço gasto ao avaliar cada regra extraída. Dessa forma, o presente trabalho desenvolveu o componente ER, o qual elimina regras de associação redundantes e irrelevantes. Cada regra válida é utilizada pelo componente TARE com o objetivo de detectar inconsistências. TARE introduz o conceito de STARs (regras de associação temporais específicas), as quais são utilizadas para detectar possíveis inconsistências. Cada STAR considerada relevante é utilizada como entrada para o componente TCI com o intuito de obter correlações temporais para (i) detectar possíveis inconsistências e (ii) auxiliar a popular a BC. Experimentos realizados demonstraram que as regras de associação e a correlação temporal são capazes de expandir a base de conhecimento, diminuindo a quantidade de valores ausentes. Além disso, ambos os componentes TARE e TCI foram eficientes no processo para detectar possíveis inconsistências na base de dados. Por fim, o componente ER reduziu em mais de 30% o número de regras sem perda no processo de popular a BC.Não recebi financiamentoporUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-Graduação em Ciência da Computação - PPGCCUFSCarRegras de associaçãoGrandes bases de conhecimentoRegras de associação temporais específicasCorrelação temporalDetecção de inconsistênciasRegras redundantesRegras irrelevantesAssociation rulesLarge knowledge basesSpecific temporal association rulesTemporal correlationsInconsistency detectionRedundant rulesIrrelevant rulesCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAORegras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimentoAssociation rules and temporal correlations to populate and detect inconsistencies in large knowledge basesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisOnline6c142165-1935-4e21-8c88-f27f8c42b0c1info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARLICENSElicense.txtlicense.txttext/plain; charset=utf-81957https://repositorio.ufscar.br/bitstream/ufscar/9490/3/license.txtae0398b6f8b235e40ad82cba6c50031dMD53ORIGINALMIANI_Rafael_2018.pdfMIANI_Rafael_2018.pdfapplication/pdf1448485https://repositorio.ufscar.br/bitstream/ufscar/9490/4/MIANI_Rafael_2018.pdf179c88762892e831f993ee283fcb07a2MD54TEXTMIANI_Rafael_2018.pdf.txtMIANI_Rafael_2018.pdf.txtExtracted texttext/plain305044https://repositorio.ufscar.br/bitstream/ufscar/9490/5/MIANI_Rafael_2018.pdf.txta1f15d70d231043066430cc738dfab8fMD55THUMBNAILMIANI_Rafael_2018.pdf.jpgMIANI_Rafael_2018.pdf.jpgIM Thumbnailimage/jpeg8908https://repositorio.ufscar.br/bitstream/ufscar/9490/6/MIANI_Rafael_2018.pdf.jpge6c8919102422e1aeb0de1a21927d132MD56ufscar/94902023-09-18 18:31:13.355oai:repositorio.ufscar.br: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Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:31:13Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.por.fl_str_mv |
Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento |
dc.title.alternative.eng.fl_str_mv |
Association rules and temporal correlations to populate and detect inconsistencies in large knowledge bases |
title |
Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento |
spellingShingle |
Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento Miani, Rafael Garcia Leonel Regras de associação Grandes bases de conhecimento Regras de associação temporais específicas Correlação temporal Detecção de inconsistências Regras redundantes Regras irrelevantes Association rules Large knowledge bases Specific temporal association rules Temporal correlations Inconsistency detection Redundant rules Irrelevant rules CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO |
title_short |
Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento |
title_full |
Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento |
title_fullStr |
Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento |
title_full_unstemmed |
Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento |
title_sort |
Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento |
author |
Miani, Rafael Garcia Leonel |
author_facet |
Miani, Rafael Garcia Leonel |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/9487235096598355 |
dc.contributor.author.fl_str_mv |
Miani, Rafael Garcia Leonel |
dc.contributor.advisor1.fl_str_mv |
Hruschka Júnior, Estevam Rafael |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2097340857065853 |
dc.contributor.authorID.fl_str_mv |
5530e5cc-cbf4-4ceb-8835-399dc418545f |
contributor_str_mv |
Hruschka Júnior, Estevam Rafael |
dc.subject.por.fl_str_mv |
Regras de associação Grandes bases de conhecimento Regras de associação temporais específicas Correlação temporal Detecção de inconsistências Regras redundantes Regras irrelevantes |
topic |
Regras de associação Grandes bases de conhecimento Regras de associação temporais específicas Correlação temporal Detecção de inconsistências Regras redundantes Regras irrelevantes Association rules Large knowledge bases Specific temporal association rules Temporal correlations Inconsistency detection Redundant rules Irrelevant rules CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Association rules Large knowledge bases Specific temporal association rules Temporal correlations Inconsistency detection Redundant rules Irrelevant rules |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO |
description |
Large growing knowledge bases have been an interesting field in many researches in the past few years. Most techniques focus on constructing algorithms to help a Knowledge Base (KB) automatically (or semi automatically) expands. However, many tools used to expand the KBs can extract incomplete or incorrect data, turning the KB inconsistent. In this way, this work has the objective to expand large knowledge bases as well as detect inconsistencies on them. To accomplish that, an association rule mining algorithm and temporal correlation are used. Applying an algorithm to extract association rules in large knowledge bases, the missing value problem need to be considered, once these bases grow day to day, and do not have all of the data. Therefore, a new parameter was created to perform the support calculation, the MSC parameter, to deal with missing values. Besides, a major problem on using association rules is the effort spent to analyze each extracted rule. Thus, this work developed ER component, which eliminates redundant and irrelevant association rules. Each valid rule is used by TARE component with the purpose of detecting inconsistencies. TARE introduces the concept of STARs (specific temporal association rules), which are used to detect possible inconsistencies. Each relevant STAR is used as an input to TCI component in order to get temporal correlations to (i) detect possible inconsistencies and (ii) to help populating the KB. Experiments showed that the association rules and the temporal correlation are capable to expand the knowledge base, decreasing the amount of missing values. Moreover, both TARE and TCI components were efficient in the process of detecting possible inconsistencies in the data set. Finally, the ER component reduced the number of rules in more then 30% without any lost in the process of populating the KB. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017-12-20 |
dc.date.accessioned.fl_str_mv |
2018-02-27T19:55:50Z |
dc.date.available.fl_str_mv |
2018-02-27T19:55:50Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
MIANI, Rafael Garcia Leonel. Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento. 2017. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9490. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/9490 |
identifier_str_mv |
MIANI, Rafael Garcia Leonel. Regras de associação e correlação temporal para popular e detectar Inconsistências em grandes bases de conhecimento. 2017. Tese (Doutorado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2017. Disponível em: https://repositorio.ufscar.br/handle/ufscar/9490. |
url |
https://repositorio.ufscar.br/handle/ufscar/9490 |
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openAccess |
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Universidade Federal de São Carlos Câmpus São Carlos |
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Programa de Pós-Graduação em Ciência da Computação - PPGCC |
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UFSCar |
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Universidade Federal de São Carlos Câmpus São Carlos |
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