Retrofitting Distributional Embeddings to Knowledge Graphswith Functional Relations
Table 8: Structure of the SNOMED-CT knowledge graph.
['[BOLD] Edge Type associated_clinical_finding', '[BOLD] Count 493258', '[BOLD] Edge Type child', '[BOLD] Count 242130']
SNOMED-CT is an ontology of clinical healthcare terms and concepts including diseases, treatments, anatomical terms, and many other types of entities. To create distributional embeddings, we first link each SNOMED-CT concept to a set of Wikipedia articles by indexing the associated search terms in WikiData. We aggregate each article set by the method of \newcitearora2016simple, which performs TF-IDF weighted aggregation of pre-trained term embeddings to create sophisticated distributional embeddings of SNOMED-CT concepts. This creates a single 300-dimensional vector for each entity.