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| 4. |  | PENA, M. R.; SILVA, N. M. da; MORAIS, E. G. F. de; NORONHA, A. C. da S.; VASCONCELOS, G. J. N. de; RODRIGUES, J. C. V.; LEMOS, W. de P. Citros. In: SILVA, N. M. da; ADAIME, R.; ZUCCHI, R. A. (Ed.). Pragas agrícolas e florestais na Amazônia. Brasília, DF: Embrapa, 2016. Pt. 1, cap. 6, p. 107-135.| Biblioteca(s): Embrapa Amazônia Oriental; Embrapa Roraima. |
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| 7. | | ROGGIA, S.; GUEDES, J. V. C.; KUSS-ROGGIA, R.C.R.; VASCONCELOS, G. J. N. de; FERREIRA, D. N. M.; DELALIBERA JUNIOR, I. Ácaros predadores e o fungo Neozygites floridana associados a tetraniquídeos em soja no Rio Grande do Sul. Pesquisa Agropecuária Brasileira, Brasília, v.44, n. 1, p. 107-110, jan. 2009.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia; Embrapa Unidades Centrais. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
31/07/2012 |
|
Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
LEITE, M. A. A.; RICARTE, I. L. M. |
|
Afiliação: |
MARIA ANGELICA ANDRADE LEITE, CNPTIA; IVAN LUIZ MARQUES RICARTE, FEEC/Unicamp. |
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Título: |
Relating ontologies with a fuzzy information model. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Knowledge and Information Systems, London, 2012. |
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Páginas: |
Não paginado. |
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DOI: |
10.1007/s10115-012-0482-0 |
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Idioma: |
Inglês |
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Conteúdo: |
More people than ever before have access to information with the World Wide Web; information volume and number of users both continue to expand. Traditional search methods based on keywords are not effective, resulting in large lists of documents, many of which unrelated to users? needs. One way to improve information retrieval is to associate meaning to users? queries by using ontologies, knowledge bases that encode a set of concepts about one domain and their relationships. Encoding a knowledge base using one single ontology is usual, but a document collection can deal with different domains, each organized into an ontology. This work presents a novel way to represent and organize knowledge, from distinct domains, using multiple ontologies that can be related. The model allows the ontologies, as well as the relationships between concepts from distinct ontologies, to be represented independently. Additionally, fuzzy set theory techniques are employed to deal with knowledge subjectivity and uncertainty. This approach to organize knowledge and an associated query expansion method are integrated into a fuzzy model for information retrieval based on multi-related ontologies. The performance of a search engine using this model is compared with another fuzzy-based approach for information retrieval, and with the Apache Lucene search engine. Experimental results show that this model improves precision and recall measures. |
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Palavras-Chave: |
Modelo fuzzy; Ontologias; Organização da informação. |
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Thesaurus NAL: |
Fuzzy logic. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02029naa a2200205 a 4500
001 1929972
005 2020-01-08
008 2012 bl uuuu u00u1 u #d
024 7 $a10.1007/s10115-012-0482-0$2DOI
100 1 $aLEITE, M. A. A.
245 $aRelating ontologies with a fuzzy information model.$h[electronic resource]
260 $c2012
300 $aNão paginado.
520 $aMore people than ever before have access to information with the World Wide Web; information volume and number of users both continue to expand. Traditional search methods based on keywords are not effective, resulting in large lists of documents, many of which unrelated to users? needs. One way to improve information retrieval is to associate meaning to users? queries by using ontologies, knowledge bases that encode a set of concepts about one domain and their relationships. Encoding a knowledge base using one single ontology is usual, but a document collection can deal with different domains, each organized into an ontology. This work presents a novel way to represent and organize knowledge, from distinct domains, using multiple ontologies that can be related. The model allows the ontologies, as well as the relationships between concepts from distinct ontologies, to be represented independently. Additionally, fuzzy set theory techniques are employed to deal with knowledge subjectivity and uncertainty. This approach to organize knowledge and an associated query expansion method are integrated into a fuzzy model for information retrieval based on multi-related ontologies. The performance of a search engine using this model is compared with another fuzzy-based approach for information retrieval, and with the Apache Lucene search engine. Experimental results show that this model improves precision and recall measures.
650 $aFuzzy logic
653 $aModelo fuzzy
653 $aOntologias
653 $aOrganização da informação
700 1 $aRICARTE, I. L. M.
773 $tKnowledge and Information Systems, London, 2012.
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