I've been doing some work on link prediction in knowledge graphs recently with poor results on real-world data. These methods don't necessarily require a huge amount of data but they are very sensitive to noise and the 'density' of dataset. The benchmark datasets are, in essence, very easy to get good performance on. It's a real shame that metrics for these methods' tolerance of noise and sparsity are not reported because these are going to be present in almost any real-world dataset in far greater quantities than current benchmarks.