How to extract knowledge of Qualitative Data from Big Textual Data

Abstract

In this article , we will analyze how to obtain pertinent Information in the form of Qualitative Data graphically represented from unstructured Big Textual data . Unstructured data refers to information that either does not have a pre - defined data model or is not organized in a pre - defined manner ( 80 - 90 % of all information ). Obviously , it is not useful to accumulate large amounts of information if we cannot find a particular piece of information . The current methods prove to be expensive and the results are too often inappropriate . The goal of the research described here is to present an approach for automating the detection and the extraction of meaning from unstructured data using its normalized part : Web of data & Linked Open data ( LOD ). On the other hand , in structured indexes , classification systems , thesauri , conceptual structures or semantic networks , relationships are too often vague . One possible