Modeling Drug-Disease Relations with Linguistic and Knowledge Graph Constraints

Bruno Godefroy; Christopher Potts

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arXiv · 1904.00313

Modeling Drug-Disease Relations with Linguistic and Knowledge Graph Constraints

Bruno Godefroy·Christopher Potts

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Abstract

FDA drug labels are rich sources of information about drugs and drug-disease relations, but their complexity makes them challenging texts to analyze in isolation. To overcome this, we situate these labels in two health knowledge graphs: one built from precise structured information about drugs and diseases, and another built entirely from a database of clinical narrative texts using simple heuristic methods. We show that Probabilistic Soft Logic models defined over these graphs are superior to text-only and relation-only variants, and that the clinical narratives graph delivers exceptional results with little manual effort. Finally, we release a new dataset of drug labels with annotations for five distinct drug-disease relations.

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Bruno Godefroy, Christopher Potts. 2019-03-31. Modeling Drug-Disease Relations with Linguistic and Knowledge Graph Constraints. https://arxiv.org/abs/1904.00313

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