Abstract
Unapparent Information Revelation refers to the task in the text mining of a document collection of revealing interesting information other than that which is explicitly stated. It focuses on detecting possible links between concepts across multiple text documents by generating a graph that matches the evidence trail found in the documents. A Concept Chain Graph is a statistical technique to find links in snippets of information where singularly each small piece appears to be unconnected. In relation to algorithm performance, Latent Semantic Indexing and the Contextual Network Graph are found to be comparable to the Concept Chain Graph. These aspects are explored and discussed. In this paper, a review is performed on these three similarly grounded approaches. The Concept Chain Graph is proposed as being suited to extracting interesting relations among concepts that co-occur within text collections due to its prominent ability to construct a directed graph, representing the evidence trail. It is the baseline study for our hybrid Concept Chain Graph approach.