  Knowledge graphs have shown promise for several cybersecurity tasks, such as
vulnerability assessment and threat analysis. In this work, we present a new
method for constructing a vulnerability knowledge graph from information in the
National Vulnerability Database (NVD). Our approach combines named entity
recognition (NER), relation extraction (RE), and entity prediction using a
combination of neural models, heuristic rules, and knowledge graph embeddings.
We demonstrate how our method helps to fix missing entities in knowledge graphs
used for cybersecurity and evaluate the performance.
