WordNet ontology based model for information retrieval

In the area of information retrieval, the dimension of document vectors plays an important role. Firstly, with higher index dimensions the indexing structures suffer from the "curse of dimensionality" and their efficiency rapidly decreases. Secondly, we may not use exact words when looking for a document, thus we miss some relevant documents. LSI (Latent Semantic Indexing) is a numerical method, which discovers latent semantic in documents by creating concepts from existing terms. In this article we present a basic method of mapping LSI concepts on given ontology (WordNet), used both for retrieval recall improvement and dimension reduction. We offer experimental results for this method on a subset of TREC collection, consisting of Los Angeles Times articles.