Lattice-context based digital paper search

Based on concept lattice, this paper proposes a new digital paper search paradigm that ranks search outputs, while controlling the diversity of keyword-based search query output topics. During pre-querying, papers are assigned into pre-specified lattice-based contexts patterns, and query-independent context scores are attached to papers with respect to the assigned contexts. When a query is executed, relevant contexts are selected, search is performed within the selected contexts, context scores of papers are revised into relevancy scores with respect to the query and the context that they are in, and query outputs are ranked within each relevant context. Our experiments indicate that the proposed lattice context-based search approach produces search results with up to 50% higher precision, and reduces the query output size by up to 60% than CNKI search.