An Ensemble Link Prediction Model for Sparse Knowledge Graphs With Drifting Entity

This research addresses the problem of incomplete knowledge graphs (databases of connected facts about entities) by proposing a new model called TEDD that predicts missing relationships between entities. The model combines both structural information from the graph and text information, and uses a specialized transformer technique (BERT, a language processing model) to reduce computational costs and handle entities that change over time in dynamic knowledge graphs.