Evaluating Different Ranking Functions for Context-Based Literature Search

Context-based literature digital library search is a new search paradigm that creates an effective ranking of query outputs by controlling query output topic diversity. We define contexts as pre-specified ontology-based terms and locate the paper set of a context based on semantic properties of the context (ontology) term. In order to pro-vide a comparative assessment of papers in a context and effectively rank papers returned as search outputs, pres-tige scores are attached to all papers with respect to their assigned contexts. In this paper, we present three different prestige score (ranking) functions for the context-based environment, namely, citation-based, text-based, and pat-tern-based score functions. Using biomedical publications as the test case and Gene Ontology as the context hierar-chy, we have evaluated the proposed ranking functions in terms of their accuracy and separability. We have found that text-based and pattern-based score functions yield better accuracy and separability than citation-based score functions. 1