GRAPHENE

Sendong Zhao, Chang Su, Andrea Sboner, Fei Wang
2019 Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19  
Effective biomedical literature retrieval (BLR) plays a central role in precision medicine informatics. In this paper, we propose GRAPHENE, which is a deep learning based framework for precise BLR. GRAPHENE consists of three main different modules 1) graph-augmented document representation learning; 2) query expansion and representation learning and 3) learning to rank biomedical articles. The graph-augmented document representation learning module constructs a document-concept graph containing
more » ... biomedical concept nodes and document nodes so that global biomedical related concept from external knowledge source can be captured, which is further connected to a BiLSTM so both local and global topics can be explored. Query expansion and representation learning module expands the query with abbreviations and different names, and then builds a CNN-based model to convolve the expanded query and obtain a vector representation for each query. Learning to rank minimizes a ranking loss between biomedical articles with the query to learn the retrieval function. Experimental results on applying our system to TREC Precision Medicine track data are provided to demonstrate its effectiveness.
doi:10.1145/3357384.3358038 dblp:conf/cikm/ZhaoSSW19 fatcat:hpll7xork5em7jkoz4kz3m3gry