利用中介关系提升因果链预测准确率
HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph
- 将因果链补全建模为超关系知识图谱完成问题
- 在CLEVRER-Humans数据集上平均提升5.94%的mRR
- 特别适合需要推理中间机制的因果分析任务
因果网络常因观测数据缺失导致因果链接不完整。现有基于知识图谱链接预测的方法未考虑中介因果链（如A→B→C中B作为中介）的影响。本文提出HyperCausalLP，通过构建包含中介信息的超关系知识图谱，训练链接预测模型以补全缺失因果链接。该方法在因果基准数据集CLEVRER-Humans上评估，相比传统方法平均提升5.94%的均值倒数排名（mRR），验证了中介关系对因果推断的关键作用。
原文摘要 · Abstract (English)
Causal networks are often incomplete with missing causal links. This is due to various issues, such as missing observation data. Recent approaches to the issue of incomplete causal networks have used knowledge graph link prediction methods to find the missing links. In the causal link A causes B causes C, the influence of A to C is influenced by B which is known as a mediator. Existing approaches using knowledge graph link prediction do not consider these mediated causal links. This paper presents HyperCausalLP, an approach designed to find missing causal links within a causal network with the help of mediator links. The problem of missing links is formulated as a hyper-relational knowledge graph completion. The approach uses a knowledge graph link prediction model trained on a hyper-relational knowledge graph with the mediators. The approach is evaluated on a causal benchmark dataset, CLEVRER-Humans. Results show that the inclusion of knowledge about mediators in causal link prediction using hyper-relational knowledge graph improves the performance on an average by 5.94% mean reciprocal rank.
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