scenario(s). We compared the proposed method with traditional statistical expansion that expands terms which are statistically correlated but not necessarily scenario specific. Our study on two standard testbeds shows that the knowledge-based method, by providing scenario-specific expansion, yields notable

on the OHSUMED testbed shows that the knowledge-based method which results in scenario-specific expansion is able to improve more than 5 % over the statistical method on average, and about 10 % for queries that mention certain scenarios, such as “treatment of a disease ” and “differential diagnosis of a symptom/disease.”

Knowledge-based query expansion to support

Medical free-text queries often share the same scenario. A scenario represents a repeating task in healthcare. For example, a specific scenario is searching for treatment methods for a specific dis-ease, where “treatment ” is a term indicating the scenario. To support scenario-specific retrieval

document collections. Second, we use a phrase-indexed patient report to automatically form a query, and expand the query with scenario-specific phrases derived from a knowledge base. Experimental results reveal that the phrase-based indexing and the knowledge-based query expansion together, yield scenario-specific

than can be accounted for by the (weak-coupling) bottom-up thermalization scenario of Ref. [2]. However, as first pointed out by Arnold et al. [3], this scenario did not include the inevitable presence of plasma instabilities in an anisotropic quark-gluon plasma [4,5]. In fact, these are even the most

and soil biomass estimates, to provide carbon payback estimates that are more regionally specific than those in previous studies. Using this cropland database, we also estimate carbon payback times under different scenarios of future crop yields, biofuel technologies, and petroleum sources. Under current

. Recently, some extensions to the original RM meth-ods have been proposed to apply query expansion in other scenarios, such as opinion retrieval. Such approaches rely on mixture models that combine the query expansion provided by Relevance Models with opin-ionated terms obtained from external resources (e

-works and characterize, in very general scenarios, which produce graph classes of bounded expansion. We show that, with high probability, (1) Erdős–Rényi random graphs, generalized to have non-uniform edge probabilities and start from any bounded-degree graph, have bounded expansion; (2) the Molloy–Reed configuration

the existence and location of an outbreak of fire in a national forest. Questions of more interest to national security involve detection of radiological or biological hazards, hidden mines and munitions, or specific individuals in a crowd. All of these scenarios pose difficult and challenging data management