Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs. Hence, learning from and with graph embeddings has drawn a great deal of research interest recently, but only ad hoc solutions have been obtained this far. In this paper, we conjecture that the one-shot supervised learning mechanism is a bottleneck in improving the performance of the graph embedding learning algorithms, and propose to extend this by introducing a multi-shot unsupervised learning framework. Empirical results on several real-world data set show that the proposed model consistently and significantly outperforms existing state-of-the-art approaches on knowledge base completion and graph based multi-label classification tasks.
Generalized Graph Embedding Models A generalized knowledge graph embedding approach which learns the embeddings based on three different simultaneous objectives, and performs on par or even outperforms existing state-of-the art approaches.

 Tackles the task of learning embeddings of multi-relational graphs using a neural network Proposes a new method, GEN, to compute embeddings of multirelationship graphs, particularly that so-called E-Cells and R-Cells can answer queries of the form (h,r,?),(?r,t), and (h,?,t)