Eventuality Centric Knowledge Graph

Eventuality-centric knowledge graphs (EVKGs) represent knowledge focused on events, states, and activities, rather than solely on entities, enabling more nuanced reasoning about narratives and situations. Current research emphasizes developing methods to effectively query and reason over these graphs, often employing graph neural networks (GNNs) and large language models (LLMs) to handle complex logical relationships and implicit constraints like temporal ordering. This approach aims to improve machine understanding of narratives and complex scenarios by grounding textual information within a structured knowledge base, leading to more accurate and interpretable results in applications like narrative reasoning and question answering.