Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
Abstract
Network Embedding (NE) methods, which
map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated
with the nodes, e.g., text describing the nodes.
Recent attempts to combine the two sources of
information only consider local network structure. We extend NODE2VEC, a well-known NE
method that considers broader network structure, to also consider textual node descriptors
using recurrent neural encoders. Our method
is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.