Searching Recurrent Architecture for Knowledge Graph Embedding

Yongqi Zhang

Abstract: Knowledge graph (KG) embedding is well-known in learning representations of KGs. Many models have been proposed to learn the interactions between enti- ties and relations of the triplets. However, long-term information among multiple triplets is also important to KG. In this work, based on the relational paths, which are composed of a sequence of triplets, we define the Interstellar as a recurrent neural architecture search problem for the short-term and long-term information along the paths. First, we analyze the difficulty of using a unified model to work as the Interstellar. Then, we propose to search for recurrent architecture as the Interstellar for different KG tasks. A case study on synthetic data illustrates the importance of the defined search problem. Experiments on real datasets demon- strate the effectiveness of the searched models and the efficiency of the proposed hybrid-search algorithm.