Finding Related Papers in Literature Digital Libraries
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.
ECDL