Semantic Ranking and Result Visualization for Life Sciences Publications

An ever-increasing amount of data and semantic knowledge in the domain of life sciences is bringing about new data management challenges. In this paper we focus on adding the semantic dimension to literature search, a central task in scientific research. We focus our attention on PubMed, the most significant bibliographic source in life sciences, and explore ways to use high-quality semantic annotations from the MeSH vocabulary to rank search results. We start by developing several families of ranking functions that relate a search query to a document's annotations. We then propose an efficient adaptive ranking mechanism for each of the families. We also describe a two-dimensional Skyline-based visualization that can be used in conjunction with the ranking to further improve the user's interaction with the system, and demonstrate how such Skylines can be computed adaptively and efficiently. Finally, we present a user study that demonstrates the effectiveness of our ranking. We use the full PubMed dataset and the complete MeSH ontology in our experimental evaluation