Abstract:
We study an automatic hypothesis generation (HG) problem, which involves discovering meaningful implicit connections between scientific terms like diseases, chemicals, drugs, and genes extracted from biomedical publication databases.
Most previous studies of this problem used static term information and ignored scientific term relation temporal dynamics. In recent studies that considered dynamics, they learned scientific term representations rather than term-pair relations.
Since the HG problem is to predict term-pair connections, it is more important to know how the connections were formed (in a dynamic process) than with whom they are connected. This HG problem is a dynamic attributed graph connectivity prediction.
Capturing node-pair (term-pair) temporal evolution is crucial. T-PAIR encodes the temporal node-pair relationship using the graphical structure and node attribute. The proposed model is tested on three graphs of Pubmed papers from Neurology, Immunotherapy, and Virology published until 2019.
Predicting future term-pair relations between millions of seen terms (transductive) and unseen terms (inductive) were evaluated. Experiments and case studies prove the model works.
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