Reviews: Quaternion Knowledge Graph Embeddings

The paper attempts learn better entity and relation embeddings for knowledge graphs. In this regard, the authors employ quarternion algebra with Hamilton product, which is used as the scoring function for knowledge triplets. Hamilton product is asymmetric, which is claimed to be beneficial for modeling directed egdes in a knowledge graph. Further the paper outperforms many well established methods and the authors seem to have done an exhaustive set of experiments. However, all the reviewers are in consensus that motivation for the use of quarternions is not clear, e.g. the paper does a poor job in demonstrating how does more degrees of freedom in rotation help in learning better embedding.