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Registro Completo |
Biblioteca(s): |
Embrapa Caprinos e Ovinos. |
Data corrente: |
01/09/2017 |
Data da última atualização: |
01/09/2017 |
Autoria: |
AVILA, S. C.; RODRIGUEZ, N. M. |
Título: |
Valor nutritivo do bagaço de cana de açucar hidrolisado, para ruminantes. |
Ano de publicação: |
1990 |
Fonte/Imprenta: |
In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 27., 1990, Campinas. Anais... Piracicaba: SBZ, 1990. p. 98. |
Idioma: |
Português |
Thesagro: |
Alimento alternativo; Alimento para animal; Cana de açúcar; Nutrição animal; Valor nutritivo. |
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
Marc: |
LEADER 00603naa a2200181 a 4500 001 2074855 005 2017-09-01 008 1990 bl uuuu u00u1 u #d 100 1 $aAVILA, S. C. 245 $aValor nutritivo do bagaço de cana de açucar hidrolisado, para ruminantes. 260 $c1990 650 $aAlimento alternativo 650 $aAlimento para animal 650 $aCana de açúcar 650 $aNutrição animal 650 $aValor nutritivo 700 1 $aRODRIGUEZ, N. M. 773 $tIn: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 27., 1990, Campinas. Anais... Piracicaba: SBZ, 1990. p. 98.
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Embrapa Caprinos e Ovinos (CNPC) |
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Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
31/07/2012 |
Data da última atualização: |
08/01/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 2 |
Autoria: |
LEITE, M. A. A.; RICARTE, I. L. M. |
Afiliação: |
MARIA ANGELICA ANDRADE LEITE, CNPTIA; IVAN LUIZ MARQUES RICARTE, FEEC/Unicamp. |
Título: |
Relating ontologies with a fuzzy information model. |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
Knowledge and Information Systems, London, 2012. |
Páginas: |
Não paginado. |
DOI: |
10.1007/s10115-012-0482-0 |
Idioma: |
Inglês |
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. |
Palavras-Chave: |
Modelo fuzzy; Ontologias; Organização da informação. |
Thesaurus NAL: |
Fuzzy logic. |
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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