提出上下文池化方法，精准筛选知识图谱链接预测的关联邻居
Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
- 基于查询动态筛选逻辑相关邻居，避免无效信息聚合
- 在48个实验中42次达到最优性能，跨多种数据集表现稳定
- 适用于训练未见实体的归纳场景，适合实际部署
近期研究表明，传统图神经网络在知识图谱链接预测中，基础聚合方式对模型性能影响有限。本文提出一种新方法——上下文池化（Context Pooling），是首个在知识图谱中应用图池化的方案。该方法首次实现归纳设置下生成查询相关的子图，其中测试实体在训练时未出现。通过设计邻域精确度与召回率两个指标，评估邻居与查询的逻辑相关性，从而精准识别出对预测有贡献的邻居。本方法具有通用性，已应用于两种前沿模型，在三个公开数据集上进行验证，在48组实验中42次达到最优结果。
原文摘要 · Abstract (English)
Recent investigations on the effectiveness of Graph Neural Network (GNN)-based models for link prediction in Knowledge Graphs (KGs) show that vanilla aggregation does not significantly impact the model performance. In this paper, we introduce a novel method, named Context Pooling, to enhance GNN-based models' efficacy for link predictions in KGs. To our best of knowledge, Context Pooling is the first methodology that applies graph pooling in KGs. Additionally, Context Pooling is first-of-its-kind to enable the generation of query-specific graphs for inductive settings, where testing entities are unseen during training. Specifically, we devise two metrics, namely neighborhood precision and neighborhood recall, to assess the neighbors' logical relevance regarding the given queries, thereby enabling the subsequent comprehensive identification of only the logically relevant neighbors for link prediction. Our method is generic and assessed by being applied to two state-of-the-art (SOTA) models on three public transductive and inductive datasets, achieving SOTA performance in 42 out of 48 settings.
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