Graph-based text representation and knowledge discovery

For information retrieval and text-mining, a robust scalable framework is required to represent the information extracted from documents and enable visualization and query of such information. One very widely used model is the vector space model which is based on the bag-of-words approach. However, it suffers from the fact that it loses important information about the original text, such as information about the order of the terms in the text or about the frontiers between sentences or paragraphs. In this paper, we propose a graph-based text representation, which is capable of capturing (i) Term order (ii) Term frequency (iii) Term co-occurrence (iv) Term context in documents. We also apply the graph model into our text mining task, which is to discover unapparent associations between two and more concepts (e.g. individuals) from a large text corpus. Counterterrorism corpus is used to evaluate the performance of various retrieval models, which demonstrates feasibility and effectiveness of graphic text representation in information retrieval and text mining.