  This paper presents an ontology-driven treatment article retrieval system
developed and experimented using the data and ground truths provided by the
TREC 2017 precision medicine track. The key aspects of our system include:
meaningful integration of various disease, gene, and drug name ontologies,
training of a novel perceptron model for article relevance labeling, a ranking
module that considers additional factors such as journal impact and article
publication year, and comprehensive query matching rules. Experimental results
demonstrate that our proposed system considerably outperforms the results of
the best participating system of the TREC 2017 precision medicine challenge.
