A Semantic Approach to Uncovering Implicit Relationships in Textual Databases

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 number of false relationships generated by most of the available processes. A semantic approach that supports the understanding of the relationships may bridge this gap. Thus, the objective of this work is to support the identification of implicit relationships between concepts present in different texts, considering the verbal semantics of relationships. To this end, analysis based on association rules were used together with metrics from complex networks and a verbal semantics approach. 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 causal relationships.