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Effective biomedical literature retrieval (BLR) plays a central role inprecision medicine informatics. In this paper, we propose GRAPHENE, which is adeep learning based framework for precise BLR. GRAPHENE consists of three maindifferent modules 1) graph-augmented document representation learning; 2) queryexpansion and representation learning and 3) learning to rank biomedicalarticles. The graph-augmented document representation learning moduleconstructs a document-concept graph containing biomedical concept nodes anddocument nodes so that global biomedical related concept from externalknowledge source can be captured, which is further connected to a BiLSTM soboth local and global topics can be explored. Query expansion andrepresentation learning module expands the query with abbreviations anddifferent names, and then builds a CNN-based model to convolve the expandedquery and obtain a vector representation for each query. Learning to rankminimizes a ranking loss between biomedical articles with the query to learnthe retrieval function. Experimental results on applying our system to TRECPrecision Medicine track data are provided to demonstrate its effectiveness.

GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment