Exploring the Potential of Semantic Relatedness in Information Retrieval

Employing lexical-semantic knowledge in information retrieval (IR) is recognised as a promising way to go beyond bag-of-words approaches to IR. However, it has not yet become a standard component of IR systems due to many difficulties which arise when knowledge-based methods are applied in IR. In this paper, we explore the use of semantic relatedness in IR computed on the basis of GermaNet, a German wordnet [Kunze, 2004]. In particular, we present several experiments on the German IR benchmarks GIRT’2005 (training set) and GIRT’2004 (test set) aimed at investigating the potential of semantic relatedness in IR as opposed to bag-of-words models, as implemented e.g. in Lucene [Gospodnetic and Hatcher, 2005]. These experiments shed some light upon how to combine the strengths of both models in our future work. Our evaluation results show some improvement in IR performance over the bag-of-words model, i.e. a significant increase in mean average precision of about 5 percent points for the training set, but only 1 percent increase for our test set