Updating collection representations for federated search

To facilitate the search for relevant information across a set of online distributed collections, a federated information retrieval system typically represents each collection, centrally, by a set of vocabularies or sampled documents. Accurate retrieval is therefore related to how precise each representation reflects the underlying content stored in that collection. As collections evolve over time, collection representations should also be updated to reflect any change, however, a current solution has not yet been proposed. In this study we examine both the implications of out-of-date representation sets on retrieval accuracy, as well as proposing three different policies for managing necessary updates. Each policy is evaluated on a testbed of forty-four dynamic collections over an eight-week period. Our findings show that out-of-date representations significantly degrade performance overtime, however, adopting a suitable update policy can minimise this problem

Incorporation of two terminology projects into a system for information retrieval using NLP for term expansion

In this paper, we will discuss two medical terminology projects at the University College of Ghent, Faculty of translation studies, and the benefits of combining them to provide Dutch professionals and laymen with better access to information in biomedical databases. Our first project, the MeSH Termbase Project (MTB) is aimed at health care professionals, medical translators and also patients in need of language support. The main aim of our second project, the Multilingual Glossary of Technical and Popular Medical Terms, is the simplification of the terminology used in patient information leaflets