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.

翻译：近期研究已利用知识感知方法处理自然语言理解、问答系统、推荐系统及其他任务。这些方法依赖于结构良好且大规模的知识图谱，该类图谱可服务于众多下游应用，并赋予知识感知模型常识推理能力。此类知识图谱通过关系抽取、知识图谱补全等知识获取任务构建而成。本研究旨在利用并拓展当前通过自然语言处理（NLP）领域成果从文本中提取知识、构建知识图谱的研究体系。研究重点在于如何借助基于Transformer的方法提取事件信息并实现其情境化，将其与现有本体进行匹配，从而构建基于图谱事件表示的全面知识。具体而言，通过子事件抽取构建具备子事件感知能力的事件表示，并进一步通过细粒度地点抽取丰富此类事件表示，同时通过历史相关引文的对齐实现情境化。