Background. Recent advances in computational and biological methods in last two decades have remarkably changed the scale of biomedical research and with it began the unprecedented growth in both the production of biomedical data and amount of published literature discussing it. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also pave the way to discover hitherto unknown information implicitly conveyed in the texts. Results. We developed a novel framework (named “BioEve”) that seamlessly integrates Faceted Search (Information Retrieval) with Information Extraction module to provide an interactive search experience for the researchers in life sciences. It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking. Conclusions. The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease. 1. Background Human genome sequencing marked the beginning of the era of large-scale genomics and proteomics, leading to large quantities of information on sequences, genes, interactions, and their annotations. In the same way that the capability to analyze data increases, the output by high-throughput techniques generates more information available for testing hypotheses and stimulating novel ones. Many experimental findings are reported in the -omics literature, where researchers have access to more than 20 million publications, with up to 4,500 new ones per day, available through to the widely used PubMed citation index and Google Scholar. This vast increase in available information demands novel strategies to help researchers to keep up to date with recent developments, as ad hoc querying with Boolean queries is tedious and often misses important information. Even though PubMed provides an advanced keyword search and offers useful query expansion, it returns hundreds or thousands of articles as result; these are sorted by publication date, without providing much help in selecting or drilling down to those few articles that are most relevant regarding the user’s actual question. As an example of both the amount of available information and the insufficiency of na？ve
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