Unsupervised discovery of relations for analysis of textual data

Abstract

This paper addresses the problem of analysing textual data for evidence discovery. A novel framework in which to perform evidence discovery is proposed in order to reduce the quantity of data to be analysed, aid the analysts’ exploration of the data and enhance the intelligibility of the presentation of the data. The framework combines information extraction techniques with visual exploration techniques to provide a novel approach to performing evidence discovery, in the form of an evidence discovery system. By utilising unrestricted, unsupervised information extraction techniques, the investigator does not require input queries or keywords for searching, thus enabling the investigator to analyse portions of the data that may not have been identified by keyword searches. A preliminary study was performed to assess the usefulness of a text mining approach to evidence discovery from a text corpus in comparison with a traditional information retrieval approach. It was concluded that the novel approach to text analysis for evidence discovery presented in this paper is a viable and promising approach for consideration in digital forensics. The preliminary experiment showed that the results obtained from the evidence discovery system are sensible and useful.