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

Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing