Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions. Recent GNN-based approaches formulate this task as a KG path searching problem, where messages are sequentially propagated from the seed node towards the answer nodes. However, these messages are past-oriented, and they do not consider the full KG context. To make matters worse, KG nodes often represent proper noun entities and are sometimes encrypted, being uninformative in selecting between paths. To address these problems, we propose Neural Tree Search (NuTrea), a tree search-based GNN model that incorporates the broader KG context. Our model adopts a message-passing scheme that probes the unreached subtree regions to boost the past-oriented embeddings. In addition, we introduce the Relation Frequency-Inverse Entity Frequency (RF-IEF) node embedding that considers the global KG context to better characterize ambiguous KG nodes. The general effectiveness of our approach is demonstrated through experiments on three major multi-hop KGQA benchmark datasets, and our extensive analyses further validate its expressiveness and robustness. Overall, NuTrea provides a powerful means to query the KG with complex natural language questions. Code is available at https://github.com/mlvlab/NuTrea.

翻译：多跳知识图谱问答（KGQA）是一项涉及从知识图谱（KG）中检索节点以回答自然语言问题的任务。近期基于图神经网络（GNN）的方法将该任务建模为KG路径搜索问题，其中消息从种子节点向答案节点顺序传播。然而，这些消息是面向过去的，未能考虑完整的KG上下文。更糟糕的是，KG节点通常表示专有名词实体，有时经过加密处理，在路径选择中缺乏信息量。为解决这些问题，我们提出神经树搜索（NuTrea），一种融合更广泛KG上下文的基于树搜索的GNN模型。该模型采用消息传递机制，通过探测未触及的子树区域来增强面向过去的嵌入表示。此外，我们引入关系频率-逆实体频率（RF-IEF）节点嵌入方法，该嵌入考虑全局KG上下文以更好表征模糊的KG节点。通过在三个主要多跳KGQA基准数据集上的实验，验证了本方法的普遍有效性，而深入分析进一步证明了其表达能力和鲁棒性。总体而言，NuTrea为使用复杂自然语言查询KG提供了有力工具。代码开源在 https://github.com/mlvlab/NuTrea。