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In this paper, we present a new similarity measure in the context of Information Retrieval (IR). The main objective of IR systems is to select relevant documents, related to a user‟s information need, from a collection of documents. Traditional approaches for document/query comparison use surface similarity, i.e. the comparison engine uses surface attributes (indexing terms). We propose a new method which combines the use of both surface and structural similarities with the aim of enhancing precision of top retrieved documents. In a previous work, we showed that the use of structural similarity in combination with cosine improves bare cosine ranking. In this paper, we compare our method to Okapi based on BM25 on the Cranfield collection. We show that structural similarities improve average precision and precision at top 10 retrieved documents about 50%. Experiments also address the term weighting influences on system performances.

ENHANCING HIGH PRECISION BY COMBINING OKAPI BM25 WITH STRUCTURAL SIMILARITY IN AN INFORMATION RETRIEVAL SYSTEM