An Ontology-driven Treatment Article Retrieval System for Precision Oncology
This work addresses the need for efficient and accurate article retrieval in precision oncology, offering a domain-specific solution that is incremental in nature.
The paper tackles the problem of retrieving relevant treatment articles for precision oncology by developing an ontology-driven system that integrates disease, gene, and drug ontologies and uses a novel perceptron model for relevance labeling, resulting in performance that considerably outperforms the best system in the TREC 2017 precision medicine challenge.
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.