Recovery Medical Articles Using Semantic Enrichment Method

The low success rate when retrieving information through web searches could be verified virtually in all areas of knowledge, due to the large amount of information available which raises the selection complexity for relevant articles. A query consists in chosen terms to drive the search for related documents. However, if new terms could be added in order to expand the relevance of the search, then there is what is called query semantic enrichment. This paper presents a semantic enrichment model to improve the quality of results for medical articles queries. This model knows the search context by using a repository of articles which is previously subjected to Latent Semantic Analysis and is supported by the National Cancer Institute ontology and the WordNet lexical database. In this way, new terms which are semantically related to the conducted search context, could be proposed to help raising precision when retrieving relevant articles.