论文标题
Adaprop：基于图神经网络的知识图形推理的学习自适应传播
AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning
论文作者
论文摘要
由于图神经网络（GNN）的流行，已经设计了各种基于GNN的方法来理解知识图（kgs）。基于GNN的KG推理方法的一个重要设计组成部分称为传播路径，该路径包含每个传播步骤中的一组涉及实体。现有方法使用手工设计的传播路径，忽略了实体与查询关系之间的相关性。此外，相关实体的数量将在更大的传播步骤中爆炸性地增长。在这项工作中，我们有动力学习一种自适应繁殖路径，以便在保留有希望的目标的同时滤除无关的实体。首先，我们设计了一个增量采样机制，可以通过线性复杂性保留附近的目标和层连接。其次，我们设计了基于学习的抽样分布，以识别语义相关的实体。广泛的实验表明，我们的方法具有强大，高效和语义意识。该代码可在https://github.com/lars-research/adaprop上获得。
Due to the popularity of Graph Neural Networks (GNNs), various GNN-based methods have been designed to reason on knowledge graphs (KGs). An important design component of GNN-based KG reasoning methods is called the propagation path, which contains a set of involved entities in each propagation step. Existing methods use hand-designed propagation paths, ignoring the correlation between the entities and the query relation. In addition, the number of involved entities will explosively grow at larger propagation steps. In this work, we are motivated to learn an adaptive propagation path in order to filter out irrelevant entities while preserving promising targets. First, we design an incremental sampling mechanism where the nearby targets and layer-wise connections can be preserved with linear complexity. Second, we design a learning-based sampling distribution to identify the semantically related entities. Extensive experiments show that our method is powerful, efficient, and semantic-aware. The code is available at https://github.com/LARS-research/AdaProp.
