Knowledge Graph Embeddings for NLP: From Theory to Practice

Knowledge graph embeddings are supervised learning models that learn vector representations of nodes and edges of labeled, directed multi-graphs. We describe their design rationale, and explain why they are receiving growing attention within the graph representation learning and the broader NLP communities. We highlight their limitations, open research directions, and real-world use cases. Besides a theoretical overview, we also provide a hands-on session, where we show how to use such models in practice. https://kge4nlp-coling22.github.io