We introduced relation specific transformations to substantially improve the performance of Open World Knowledge Graph Completion models. We also proposed an approach for clustering of relations to reduce the training time and memory footprint.
We propose an extension that enables any existing Knowledge Graph Completion model to predict facts about the open-world entities. This approach is more robust, more portable and has better performance than the published state of the art on most datasets. We also released a new dataset that overcomes the shortcomings of previous ones.
Standard neural networks suffer from catastrophic forgetting when they are trained on incrementally arriving stream of i.i.d. data. To combat this forgetting, one approach is to train GANs on previously arrived data and feed it to the network again. In this paper, we highlighted that this method is biased and proposed an approach to mitigate this bias and reduce the effect of catastrophic forgetting.