Transforming Textual Patterns into Knowledge

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Author(s): Hércules Antonio do Prado (Brazilian Enterprise for Agriculture Research, Brazil), José Palazzo Moreira de Oliveira (Federal University of Rio Grande do Sul, Brazil), Edilson Ferneda (Catholic University of Brasília, Brazil), Leandro Krug Wives (Federal University of Rio Grande do Sul, Brazil), Edilberto Magalhaes (Brazilian Public News Agency, Brazil)and Stanley Loh (Catholic University of Pelotas, Brazil)
Copyright: 2004
Pages: 21
Source title: Business Intelligence in the Digital Economy: Opportunities, Limitations and Risks
Source Author(s)/Editor(s): Mahesh S. Raisinghani (Texas Woman's University, USA)
DOI: 10.4018/978-1-59140-206-0.ch011

Keywords: Business & Management / Business Information Systems / Business Intelligence / Business Science Reference

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Abstract

Business Intelligence (BI) can benefit greatly from the bulk of knowledge that stays hidden in the large amount of textual information existing in the organizational environment. Text Mining (TM) is a technology that provides the support to extract patterns from texts. After interpreting these patterns, a business analyst can reach useful insights to improve the organizational knowledge. Although text represents the largest part of the available information in a company, just a small part of all Knowledge Discovery (KD) applications are in TM. By means of a case study, this chapter shows an alternative to how TM can contribute to BI. Also, a discussion on future trends and some conclusions are presented that support the effectiveness of TM as source of relevant knowledge.

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