Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts.
Despite the advances in digital healthcare systems offering curated structured knowledge, much of the critical information still lies in large volumes of unlabeled and unstructured clinical texts.
Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information.
Bilinear models, while expressive, are prone to overfitting and lead to quadratic growth of parameters in number of relations.
Fact triples are a common form of structured knowledge used within the biomedical domain.