Multi-hop Knowledge Base Question Answering (KBQA) aims to predict answers that require multi-hop reasoning from the topic entity in the question over the Knowledge Base (KB). Relation extraction is a core step in KBQA, which extracts the relation path from the topic entity to the answer entity. Compared with single-hop questions, multi-hop ones have more complex syntactic structures to understand, and multi-hop relation paths lead to a larger search space, which makes it much more challenging to extract the correct relation paths. To tackle the above challenges, most existing relation extraction approaches focus on the semantic similarity between questions and relation paths. However, those approaches only consider the word semantics of the relation names but ignore the graph semantics inside the knowledge base. As a result, their generalization ability relying on the naming rules of the relations, making it more difficult to generalize over large knowledge bases.

To address the current limitations and take advantage of the graph semantics of relations, we propose a novel translational embedding-based relation extractor that utilizes pretrained embeddings from TransE. In particular, we treat the multi-hop relation path as a translation from the first relation to the last one in the semantic space of TransE. Then we map the question into this space under the supervision of the path embeddings. To take full advantage of the pretrained graph semantics in TransE, we propose a KBQA framework that leverages pretrained relation semantics in relation extraction and pretrained entity semantics in answer selection. Our approach achieves state-of-the-art performance on two benchmark datasets, WebQuestionSP and MetaQA, demonstrating its effectiveness on the multi-hop KBQA task.

多跳知识库问答 (KBQA) 旨在通过知识库 (KB) 从问题中的主题实体预测需要多跳推理的答案。关系抽取是 KBQA 的核心步骤，抽取主题实体到答案实体的关系路径。与单跳问题相比，多跳问题具有更复杂的句法结构需要理解，并且多跳关系路径导致更大的搜索空间，这使得提取正确的关系路径更具挑战性。为了应对上述挑战，大多数现有的关系提取方法都关注问题和关系路径之间的语义相似性。然而，这些方法只考虑关系名称的词语义，而忽略了知识库中的图语义。因此，

为了解决当前的限制并利用关系的图语义，我们提出了一种新颖的基于平移嵌入的关系提取器，它利用了来自 TransE 的预训练嵌入。特别是，我们将多跳关系路径视为 TransE 语义空间中从第一个关系到最后一个关系的翻译。然后我们在路径嵌入的监督下将问题映射到这个空间。为了充分利用 TransE 中预训练的图语义，我们提出了一个 KBQA 框架，该框架在关系提取中利用预训练的关系语义，在答案选择中利用预训练的实体语义。我们的方法在两个基准数据集 WebQuestionSP 和 MetaQA 上实现了最先进的性能，证明了它在多跳 KBQA 任务上的有效性。