Experimental results on multiple benchmark knowledge bases show that ConvR achieves significant and consistent improvements against a variety of baselines.
it is not only more effective but also more efficient than state-of-the-art convolutional models, offering a 7% increase in MRR and a 6% increase in Hits@10, while saving 12% in parameter storage.