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Wang et al [39] proposed a neural architecture for biomedical ontology matching called Onto Emma [39]. It encodes a variety of descriptions, and derives large amounts of labeled data from biomedical thesaurus for training the model. Considering the problem of distinguishing semantic similarity and descriptive association on rare phrases, Kolyvakis et al [20] proposed a representation learning method: SCBOW+DAE(O) [20].
JMIR Med Inform 2021;9(8):e28212
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Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study
J Med Internet Res 2021;23(7):e25422
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A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development
The number of relations was so small that it was possible to train the embedding of all entities and relations in the same space to satisfy the training objective, which was similar to the result that the link prediction performance of Trans H was worse than Trans E on the WN18 data set used by Wang et al [6].
The results of Trans E and Pr Trans E were quite similar under the Hits@10 and NDCG@10. In particular, the NDCG@10 of Trans E was slightly better than that of Pr Trans E.
JMIR Med Inform 2020;8(5):e17645
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