Context-based literature digital collection search

We identify two issues with searching literature digital collections within digital libraries: (a) there are no effective paper-scoring and ranking mechanisms. Without a scoring and ranking system, users are often forced to scan a large and diverse set of publications listed as search results and potentially miss the important ones. (b) Topic diffusion is a common problem: publications returned by a keyword-based search query often fall into multiple topic areas, not all of which are of interest to users. This paper proposes a new literature digital collection search paradigm that effectively ranks search outputs, while controlling the diversity of keyword-based search query output topics. Our approach is as follows. First, during pre-querying, publications are assigned into pre-specified ontology-based contexts, and query-independent context scores are attached to papers with respect to the assigned contexts. When a query is posed, relevant contexts are selected, search is performed within the selected contexts, context scores of publications are revised into relevancy scores with respect to the query at hand and the context that they are in, and query outputs are ranked within each relevant context. This way, we (1) minimize query output topic diversity, (2) reduce query output size, (3) decrease user time spent scanning query results, and (4) increase query output ranking accuracy. Using genomics-oriented PubMed publications as the testbed and Gene Ontology terms as contexts, our experiments indicate that the proposed context-based search approach produces search results with up to 50% higher precision, and reduces the query output size by up to 70%.