  This paper proposes an algorithm named as PrTransH to learn embedding vectors
from real world EMR data based medical knowledge. The unique challenge in
embedding medical knowledge graph from real world EMR data is that the
uncertainty of knowledge triplets blurs the border between "correct triplet"
and "wrong triplet", changing the fundamental assumption of many existing
algorithms. To address the challenge, some enhancements are made to existing
TransH algorithm, including: 1) involve probability of medical knowledge
triplet into training objective; 2) replace the margin-based ranking loss with
unified loss calculation considering both valid and corrupted triplets; 3)
augment training data set with medical background knowledge. Verifications on
real world EMR data based medical knowledge graph prove that PrTransH
outperforms TransH in link prediction task. To the best of our survey, this
paper is the first one to learn and verify knowledge embedding on probabilistic
knowledge graphs.
