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The discovery of knowledge in textual databases is an approach that basically seeks for implicit relationships between different concepts in different documents written in natural language, in order to identify new useful knowledge. To assist in this process, this approach can count on the help of Text Mining techniques. Despite all the progress made, researchers in this area must still deal with a large volume of information and with the challenge of identifying the causal relationships between concepts in a certain field. A statistical and verbal semantic approach that supports the understanding of the semantic logic between concepts may help the extraction of relevant information and knowledge. The objective of this work is to support the user with the identification of implicit relationships between concepts present in different texts, considering their causal relationships. We propose a hybrid approach for the discovery of implicit knowledge present in a text corpus, using analysis based on association rules together with metrics from complex networks to identify relevant associations, verbal semantics to determine the causal relationships, and causal concept maps for their visualization. Through a case study, a set of texts from alternative medicine was selected and the different extractions showed that the proposed approach facilitates the identification of implicit knowledge by the user.

A New Statistical and Verbal-Semantic Approach to Pattern Extraction in Text Mining Applications