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This paper is about searching literature digital libraries to find "related" publications of a given publication. Existing approaches do not take into account publication topics in the relatedness computation, allowing topic diffusion across query output publications. In this paper, we propose a new way to measure "relatedness" by incorporating "contexts" (representing topics) of publications. We utilize existing ontology terms as contexts for publications, i.e., publications are assigned to their relevant contexts, where a context characterizes one or more publication topics. We define three ways of context-based relatedness, namely, (a) relatedness between two contexts (context-to-context relatedness) by using publications that are assigned to the contexts and the context structures in the context hierarchy, (b) relatedness between a context and a paper (paper-to-context relatedness), which is used to rank the relatedness of contexts with respect to a paper, and (c) relatedness between two papers (paper-to-paper relatedness) by using both paper-to-context and context-to-context relatedness measurements. Using existing biomedical ontology terms as contexts for genomics-oriented publications, our experiments indicate that the context-based approach is accurate, and solves the topic diffusion problem by effectively classifying and ranking related papers of a given paper based on the selected contexts of the paper.