Thursday, November 8, 2018 - 11:40am - 12:10pm
Rui Kuang (University of Minnesota, Twin Cities)
Biomedical knowledge graphs represent relations among biomedical entities and have been intensively analyzed for drug repositioning, disease gene discovery and other important medical applications. While most existing studies focus on analyzing and predicting pairwise relations, we consider learning multi-relations among the entities across many large biomedical knowledge graphs. We introduce a tensor-based framework of applying label propagation on the tensor product of multiple graphs for multi-relational learning.
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