Stochastic reranking of biomedical search results based on extracted entities

Health-related information is nowadays accessible from many sources and is one of the most searched-for topics on the internet. However, existing search systems often fail to provide users with a good list of medical search results, especially for classic (keyword-based) queries. In this paper we elaborate on whether and how we can exploit biomedicine-related entities from the emerging Web of Data for improving (through re-ranking) the results returned by a search system. The aim is to promote relevant but low-ranked hits containing entities that are important to the current search context. We introduce an approach that is based on entity extraction applied on the retrieved documents, yielding a graph of documents along with entities which in turn is analyzed probabilistically using a Random Walk-based method. The proposed approach is independent of the submitted query and the underlying retrieval models, and thus can be applied over any ranked list of medical search results. Evaluation results using the dataset of TREC Clinical Decision Support track demonstrate that the proposed approach can significantly improve the results returned by classic and widely applicable retrieval models. The results also enabled us to identify cases where the proposed re-ranking method fails to improve the ranking.