Deep Learning has been used extensively in many applications by researchers. With the increased attraction to Deep Learning, more and more unique models are created each year. However, sometimes some of the model details are not included in the publications. This makes using new Deep Learning models for research a time-consuming task for researchers. In order to tackle with this problem, we propose a prediction mechanism for the missing information in the model. By creating a dataset where the Deep Learning models are represented as knowledge graphs, we made it possible to use knowledge graph embedding algorithms which are specifically designed for eliminating missing information in a given data. We inspected 6 different algorithms and compared their performances on a small-scale experiment. After the comparison, we picked the most promising algorithm and used it for link prediction in Deep Learning models
