Knowledge graphs are useful and flexible knowledge representation
structures that can facilitate the integration of information in NLP tasks.
They are however incomplete, and not only that, but also skewed in the
type of knowledge they include. In this talk 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.