EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs
Cheng Jiayang, Lin Qiu, Chunkit Chan, Xin Liu, Yangqiu Song, Zheng, Zhang

TL;DR
EventGround is a framework that grounds free-text narratives to structured eventuality-centric knowledge graphs, improving narrative reasoning with interpretability and state-of-the-art performance.
Contribution
It introduces a comprehensive approach to grounding narratives to knowledge graphs, addressing event representation and sparsity issues, and demonstrating superior reasoning performance.
Findings
Outperforms baseline models with GNN or LLM-based reasoning.
Achieves state-of-the-art results in narrative reasoning tasks.
Provides interpretable evidence through grounded knowledge.
Abstract
Narrative reasoning relies on the understanding of eventualities in story contexts, which requires a wealth of background world knowledge. To help machines leverage such knowledge, existing solutions can be categorized into two groups. Some focus on implicitly modeling eventuality knowledge by pretraining language models (LMs) with eventuality-aware objectives. However, this approach breaks down knowledge structures and lacks interpretability. Others explicitly collect world knowledge of eventualities into structured eventuality-centric knowledge graphs (KGs). However, existing research on leveraging these knowledge sources for free-texts is limited. In this work, we propose an initial comprehensive framework called EventGround, which aims to tackle the problem of grounding free-texts to eventuality-centric KGs for contextualized narrative reasoning. We identify two critical problems in…
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Taxonomy
TopicsTopic Modeling · Semantic Web and Ontologies · Natural Language Processing Techniques
MethodsGraph Neural Network · Focus
