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| Registros recuperados : 25 | |
| 6. |  | PRADO, H. A. do; MAGALHÃES, A. R.; FERNEDA, E. Reasoning about external environment from web sources. In: Knowledge-Based and Intelligent Information and Engineering Systems 13th International Conference, KES 2009, Santiago, Chile, September 28-30, 2009, Proceedings, Part II. 5712 348-355 (Lecture Notes in Computer Science -LNCS, volume 5712).| Biblioteca(s): Embrapa Unidades Centrais. |
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| 8. | | OLIVEIRA, S. R. de M.; CABRAL, M. I. C.; FERNEDA, E.; BRASILEIRO, M. A. G. ALLOS: a tool to solve markovian models. In: INTERNATIONAL CONFERENCE APPLIED MODELLING, SIMULATION AND OPTIMIZATION, 1995, Cancun, Mexico. Proceedings... Anaheim, CA: IASTED-Acta Press, 1995. p. 53-57.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 13. | | PRADO, H. A. do; FERNEDA, E.; MORAIS, L. C. R.; LUIZ, A. J. B.; MATSURA, E. On the effectiveness of candlestick chart analysis for the Brazilian stock market. Procedia Computer Science, Valmiera, v. 22, p. 1136-1145, 2013. Edição de Proceedings of XVII International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Kitakyushu, 2013.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 14. | | PRADO, H. A. do; FERNEDA, E.; ANQUETIL, N.; TEIXEIRA, E. D'A. Counselor, a data mining based time estimation for software maintenance. In: In: VELÁSQUEZ, J. D.; RÍOS, S. A.; HOWLETT, R. J.; JAIN, L., C. (Ed.). Knowledge-Based and Intelligent Information and Engineering Systems 13th International Conference, KES 2009, Santiago, Chile, September 28-30, 2009, Proceedings, Part II. 5712 p. 364-371 (Lecture Notes in Computer Science - LNCS, 5712).| Biblioteca(s): Embrapa Unidades Centrais. |
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| 15. | | OLIVEIRA, S. R. de M.; CABRAL, M. I. C.; FERNEDA, E.; BRASILEIRO, M. A. G. Conception and development of a tool to model and solve markovian models. In: INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY, 15., 1995, Arica, Chile. Proceedings... Santiago: Sociedad Chilena de Ciencia de la Computacion, 1995. p. 351-360. Editado por Nivio Ziviani, Jose Piquer, Berthier Ribeiro e Ricardo Baeza-Yates.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 16. | | CASTILHO, W. F.; LUCENA FILHO, G. J.; PRADO, H. A. do; FERNEDA, E.; AXT, M. A conceptual model for guiding the clustering analysis. In: INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, 12., 2008, Zagreb, Croatia. Proceedings. New York: Springer-Verlag Berlin Heidelberg, 2008. (Lecture notes in computer science, 5178). pt. 2, p. 483-490.| Biblioteca(s): Embrapa Agroindústria de Alimentos. |
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| Registros recuperados : 25 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
29/06/2012 |
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Data da última atualização: |
05/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 3 |
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Autoria: |
PRADO, H. A. do; FERNEDA, E.; RODRIGUES, F. C. da L.; MARTINS, E. de S.; CARVALHO JUNIOR, O. A. de; LUIZ, A. J. B. |
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Afiliação: |
HERCULES ANTONIO DO PRADO, SGE; EDILSON FERNEDA, UNIVERSIDADE CATÓLICA DE BRASÍLIA; FRANCISCO CARLOS DA LUZ RODRIGUES, UNIVERSIDADE CATÓLICA DE BRASÍLIA; EDER DE SOUZA MARTINS, CPAC; OSMAR ABÍLIO DE CARVALHO JUNIOR, UNB; ALFREDO JOSE BARRETO LUIZ, CNPMA. |
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Título: |
Structuring taxonomies from texts: a case-study on defining soil classes. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
Lecture Notes in Computer Science, Berlin, v. 7335, p. 657-666, 2012. |
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Idioma: |
Inglês |
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Conteúdo: |
Currently, most of the information digitally available is presented in textual form and it is largely acknowledged that, in many fields, the advance of knowledge may strongly benefit from this source of information. The treatment of this vast amount of texts by means of Text Mining (TM) techniques has produced interesting information in fields like Competitive Intelligence and Bibliometry that need to make sense from textual descriptions of facts. In this paper we approach the problem of taxonomy generation from texts, a common need from a large set of scientific disciplines. Taxonomy generation refers to building a hierarchical structure that organizes concepts in a knowledge domain. We applied TM techniques to help experts in Pedology in building taxonomy from redundant soils descriptions. The motto of the application is the fact that, in the early eighties, different organizations mapped and described equivalent classes of soils from Brazilian savannas, generating redundant descriptions with different class labels. There were produced 28 soil maps that covered 4,101 descriptions of soil classes. This profusion of redundant soil descriptions clearly represents a Babel Tower that makes difficult tasks like environment management and food production. The proposed process is based in clustering analysis and runs on the soil descriptions, performing a successive refinement of the abstractions found in soil descriptions. The method builds a frame that shows, for each cluster formed, the prototype (a representative word vector) and the soil descriptions related to that cluster. The results have been analyzed by a team of experts as input information to the laborious reasoning process involved in building concepts from the semantic relations among the soil descriptions. Without a help like the present process, the experts would have to compare visually at least 4,101 × 4.100 × ?× 1 soil descriptions to define the clusters, what is much more laborious. MenosCurrently, most of the information digitally available is presented in textual form and it is largely acknowledged that, in many fields, the advance of knowledge may strongly benefit from this source of information. The treatment of this vast amount of texts by means of Text Mining (TM) techniques has produced interesting information in fields like Competitive Intelligence and Bibliometry that need to make sense from textual descriptions of facts. In this paper we approach the problem of taxonomy generation from texts, a common need from a large set of scientific disciplines. Taxonomy generation refers to building a hierarchical structure that organizes concepts in a knowledge domain. We applied TM techniques to help experts in Pedology in building taxonomy from redundant soils descriptions. The motto of the application is the fact that, in the early eighties, different organizations mapped and described equivalent classes of soils from Brazilian savannas, generating redundant descriptions with different class labels. There were produced 28 soil maps that covered 4,101 descriptions of soil classes. This profusion of redundant soil descriptions clearly represents a Babel Tower that makes difficult tasks like environment management and food production. The proposed process is based in clustering analysis and runs on the soil descriptions, performing a successive refinement of the abstractions found in soil descriptions. The method builds a frame that shows, for each cluster fo... Mostrar Tudo |
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Palavras-Chave: |
Mineração de texto; Text mining. |
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Thesagro: |
Cerrado; Solo; Taxonomia. |
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Thesaurus NAL: |
Soil taxonomy. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02714naa a2200253 a 4500
001 1927451
005 2023-01-05
008 2012 bl uuuu u00u1 u #d
100 1 $aPRADO, H. A. do
245 $aStructuring taxonomies from texts$ba case-study on defining soil classes.$h[electronic resource]
260 $c2012
520 $aCurrently, most of the information digitally available is presented in textual form and it is largely acknowledged that, in many fields, the advance of knowledge may strongly benefit from this source of information. The treatment of this vast amount of texts by means of Text Mining (TM) techniques has produced interesting information in fields like Competitive Intelligence and Bibliometry that need to make sense from textual descriptions of facts. In this paper we approach the problem of taxonomy generation from texts, a common need from a large set of scientific disciplines. Taxonomy generation refers to building a hierarchical structure that organizes concepts in a knowledge domain. We applied TM techniques to help experts in Pedology in building taxonomy from redundant soils descriptions. The motto of the application is the fact that, in the early eighties, different organizations mapped and described equivalent classes of soils from Brazilian savannas, generating redundant descriptions with different class labels. There were produced 28 soil maps that covered 4,101 descriptions of soil classes. This profusion of redundant soil descriptions clearly represents a Babel Tower that makes difficult tasks like environment management and food production. The proposed process is based in clustering analysis and runs on the soil descriptions, performing a successive refinement of the abstractions found in soil descriptions. The method builds a frame that shows, for each cluster formed, the prototype (a representative word vector) and the soil descriptions related to that cluster. The results have been analyzed by a team of experts as input information to the laborious reasoning process involved in building concepts from the semantic relations among the soil descriptions. Without a help like the present process, the experts would have to compare visually at least 4,101 × 4.100 × ?× 1 soil descriptions to define the clusters, what is much more laborious.
650 $aSoil taxonomy
650 $aCerrado
650 $aSolo
650 $aTaxonomia
653 $aMineração de texto
653 $aText mining
700 1 $aFERNEDA, E.
700 1 $aRODRIGUES, F. C. da L.
700 1 $aMARTINS, E. de S.
700 1 $aCARVALHO JUNIOR, O. A. de
700 1 $aLUIZ, A. J. B.
773 $tLecture Notes in Computer Science, Berlin$gv. 7335, p. 657-666, 2012.
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