Knowledge Graphs represent information from real-world entities and their relationships in a graph structure. Their construction, representation and completion have been an active research topics in the last few years.
On the other hand, information have been also represented through natural language in vast amounts of documents throughout the history of humanity and recently large language models have been trained to embed this textual information in dense vector spaces.
In this seminar, we are going to study the basic concepts of knowledge graphs (creation and completion) and language model training, and then we will concentrate on recent research that have tried to combine both approaches.