The published biomedical literature contains extensive amount of valuable undiscovered knowledge and hidden relations. Classical information retrieval techniques allow to access explicit information from a given collection of information, but are not able to recognize implicit connections. Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting literature. It could significantly support scientific research by identifying new connections between biomedical entities. However, most of the existing approaches are not applicable to both closed and open discovery tasks. Here, we present a model which incorporates knowledge graph, graph embedding and deep learning methods for both open and closed LBD. In this work, we showed how deep learning combining with graph embedding techniques can be applied to LBD tasks, including discovering new knowledge from unrelated literature (open LBD), and providing logical explanations for the relations between entities (closed LBD). The experimental results suggest that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.