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 provide a comparative assessment of papers in a context and effectively rank papers returned as search outputs, prestige 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 pattern-based score functions. Using biomedical publications as the test case and Gene Ontology as the context hierarchy, 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.