Answering complex logical queries on incomplete knowledge graphs (KGs) is a fundamental and challenging task in multi-hop reasoning. Recent work defines this task as an end-to-end optimization problem, which significantly reduces the training cost and enhances the generalization of the model by a pretrained link predictors for query answering. However, most existing proposals ignore the critical semantic knowledge inherently available in KGs, such as type information, which could help answer complex logical queries. To this end, we propose TypE-based Neural Link Prediction Adapter (TENLPA), a novel model that constructs type-based entity-relation graphs to discover the latent relationships between entities and relations by leveraging type information in KGs. Meanwhile, in order to effectively combine type information with complex logical queries, an adaptive learning mechanism is introduced, which is trained by back-propagating during the complex query answering process to achieve adaptive adjustment of neural link predictors. Experiments on 3 standard datasets show that TENLPA model achieves state-of-the-art performance on complex query answering with good generalization and robustness.

翻译：在不完整知识图谱上回答复杂逻辑查询是多跳推理中一项基础且具有挑战性的任务。近期工作将此任务定义为一个端到端优化问题，通过预训练链接预测器进行查询回答，显著降低了训练成本并增强了模型的泛化能力。然而，现有方法大多忽略了知识图谱中固有的关键语义知识（如类型信息），而这些知识有助于回答复杂逻辑查询。为此，我们提出了基于类型的神经链接预测适配器（TENLPA），一种通过利用知识图谱中的类型信息构建基于类型的实体-关系图以发现实体与关系间潜在联系的新型模型。同时，为有效将类型信息与复杂逻辑查询结合，引入了一种自适应学习机制，该机制通过复杂查询回答过程中的反向传播进行训练，实现对神经链接预测器的自适应调整。在3个标准数据集上的实验表明，TENLPA模型在复杂查询回答上取得了最优性能，并具有良好的泛化性和鲁棒性。