Abstract: Knowledge graphs are useful and flexible knowledge representation structures that can be useful in a variety of way — e.g. facilitating the integration of information in NLP tasks or knowledge discovery. Not only do knowledge graphs reflect our natural way of connecting things, but we can treat them both as symbolic collections, or embed them in n-dimensional spaces and transfer our linguistic problems into the algebraic domain. In this talk I will present the various facets of knowledge graphs, including their shortcomings — mainly incompleteness and imbalance –, their interaction with unstructured texts, and their embedding into n-dimensional spaces. I will present an investigation into two existing knowledge graphs – Freebase15k and WordNet18 – and show how particular characteristics influence the quality of knowledge graph embeddings, which ultimately impact knowledge graph completion and other tasks. I will also talk about knowledge discovery in knowledge graphs – as paths associated with direct relations – and how these patterns can be used for both “internal” knowledge graph completion and targeted information extraction from external textual sources.