构建首个因果知识图谱的语料库与评估框架，支持自动构建与模型选型。
WikiCausal: Corpus and Evaluation Framework for Causal Knowledge Graph Construction
- 基于维基百科和Wikidata事件概念构建因果关系语料库。
- 用Wikidata已有因果关系测召回率，用大模型替代人工评估。
- 提供可复现的评估流程，适合因果推理与知识图谱研究者使用。
近年来，通用领域和特定领域因果知识图谱的构建受到越来越多关注。这类知识图谱支持因果分析与事件预测，具有广泛的应用前景。尽管在自动化构建方面已取得显著进展，但现有评估方法或局限于低层级任务（如因果短语抽取），或依赖于小规模人工标注数据。本文提出一个用于因果知识图谱构建的语料库、任务定义与评估框架。语料库包含维基百科中与Wikidata事件概念相关的一组文章，任务是从这些文本中提取事件概念间的因果关系。评估部分利用Wikidata中已有的因果关系计算召回率，并借助大型语言模型避免人工或众包标注的需求。我们评估了一个依赖神经问答与概念链接模型的因果知识图谱构建流水线，证明该语料库和评估框架能有效识别各任务的最佳模型。相关资源已公开可用。
原文摘要 · Abstract (English)
Recently, there has been an increasing interest in the construction of general-domain and domain-specific causal knowledge graphs. Such knowledge graphs enable reasoning for causal analysis and event prediction, and so have a range of applications across different domains. While great progress has been made toward automated construction of causal knowledge graphs, the evaluation of such solutions has either focused on low-level tasks (e.g., cause-effect phrase extraction) or on ad hoc evaluation data and small manual evaluations. In this paper, we present a corpus, task, and evaluation framework for causal knowledge graph construction. Our corpus consists of Wikipedia articles for a collection of event-related concepts in Wikidata. The task is to extract causal relations between event concepts from the corpus. The evaluation is performed in part using existing causal relations in Wikidata to measure recall, and in part using Large Language Models to avoid the need for manual or crowd-sourced evaluation. We evaluate a pipeline for causal knowledge graph construction that relies on neural models for question answering and concept linking, and show how the corpus and the evaluation framework allow us to effectively find the right model for each task. The corpus and the evaluation framework are publicly available.
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