Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing
This work addresses the need for better event understanding in knowledge graphs for applications like question answering and recommendation systems, but it appears incremental as it builds on existing NLP and knowledge graph methods.
The paper tackles the problem of building comprehensive event representations by using transformer-based NLP to extract and contextualize event information, matching it to existing ontologies, and enriching it with sub-event extraction, fine-grained location extraction, and alignment of historically relevant quotes.
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.