Hypothesis generation is the problem of discovering meaningful, implicit connections in a particular domain. We focus on two application areas 1) biomedicine and the discovery of new connections between scientific terms such as diseases, chemicals, drugs, genes, 2) food pairing for discovering new connections between ingredients, taste and flavor molecules. Sony AI and its academtic partners have developed a variety of models that explore representation learning and novel link prediction models for these tasks. In the biomedical domain, we developed models able to leverage temporal data about how connections between concepts have emerged over the last 80 years. In the food domain, we deal with multi-partite graphs that link ingredients with molecule information and health aspects of ingredients. The talk will introduce hypothesis generation as a graph embedding representation learning and link prediction task. We'll present recently published models that integrate 1) variational inference for estimating priors, 2) graph embedding learning regimes and 3) application of embeddings in training ranking models.