A latent semantic indexing and WordNet based information retrieval model for digital forensics
Lan Du, Huidong Jin, Olivier De Vel, Nianjun Liu · 2008
It is well known that either domain specific or domain independent knowledge has been adopted in Information retrieval (IR) to improve the retrieval performance. In this paper, we propose a novel IR model for digital forensics by using latent semantic indexing (LSI) and WordNet as an underlying reference ontology to retrieve suspicious emails according to the semantic meaning of an investigatorpsilas query. Our model incorporates corpus independent knowledge from WordNet and corpus dependent knowledge from LSI into query expansion and reduction; and LSI is also adopted to simulate human meaning based judgement of relatedness between investigatorpsilas queries and emails. We compare the performance of the resulting LSI And WordNet based Information retrieval system (LAWIRS) with other three systems we implement, i.e. the LSI system, the Lucene system and the Lucene system with query expansion. Experimental results on several email datasets demonstrate that for short Boolean queries, LAWIRS can successfully capture their meaning and yield substantial improvements in the overall retrieval performance.