论文标题
QAGCN：通过单步隐式推理在知识图上回答多关系问题
QAGCN: Answering Multi-Relation Questions via Single-Step Implicit Reasoning over Knowledge Graphs
论文作者
论文摘要
多关系答案（QA）是一项具有挑战性的任务，在给定的问题中，通常需要在KGS中长时间的推理链，这些链条由多个关系组成。最近，在这项任务中突出使用了具有千格千克的多步推理的方法，并证明了有希望的表现。示例包括通过kg三元组和基于强化学习的kg三元组导航的逐步标签传播的方法。这些方法的主要弱点是它们的推理机制通常很复杂且难以实施或训练。在本文中，我们认为可以通过端到端的单步隐式推理来实现多关系质量检查，这更简单，更高效且易于采用。我们提出了QAGCN-基于问题的图形卷积网络（GCN）的方法，其中包括一种新型的GCN体系结构，具有与隐式推理有关的受控问题依赖性消息传播。已经进行了广泛的实验，与最先进的明确方法相比，QAGCN实现了竞争性甚至更高的性能。我们的代码和预培训模型可在存储库中提供：https：//github.com/ruijie-wang-uzh/qagcn
Multi-relation question answering (QA) is a challenging task, where given questions usually require long reasoning chains in KGs that consist of multiple relations. Recently, methods with explicit multi-step reasoning over KGs have been prominently used in this task and have demonstrated promising performance. Examples include methods that perform stepwise label propagation through KG triples and methods that navigate over KG triples based on reinforcement learning. A main weakness of these methods is that their reasoning mechanisms are usually complex and difficult to implement or train. In this paper, we argue that multi-relation QA can be achieved via end-to-end single-step implicit reasoning, which is simpler, more efficient, and easier to adopt. We propose QAGCN -- a Question-Aware Graph Convolutional Network (GCN)-based method that includes a novel GCN architecture with controlled question-dependent message propagation for the implicit reasoning. Extensive experiments have been conducted, where QAGCN achieved competitive and even superior performance compared to state-of-the-art explicit-reasoning methods. Our code and pre-trained models are available in the repository: https://github.com/ruijie-wang-uzh/QAGCN
