  Recent work has utilised knowledge-aware approaches to natural language
understanding, question answering, recommendation systems, and other tasks.
These approaches rely on well-constructed and large-scale knowledge graphs that
can be useful for many downstream applications and empower knowledge-aware
models with commonsense reasoning. Such knowledge graphs are constructed
through knowledge acquisition tasks such as relation extraction and knowledge
graph completion. This work seeks to utilise and build on the growing body of
work that uses findings from the field of natural language processing (NLP) to
extract knowledge from text and build knowledge graphs. The focus of this
research project is on how we can use transformer-based approaches to extract
and contextualise event information, matching it to existing ontologies, to
build a comprehensive knowledge of graph-based event representations.
Specifically, sub-event extraction is used as a way of creating sub-event-aware
event representations. These event representations are then further enriched
through fine-grained location extraction and contextualised through the
alignment of historically relevant quotes.
