Knowledge graph (KG) based reasoning has been regarded as an effective means for the analysis of semantic networks and is of great usefulness in areas of information retrieval, recommendation, decision-making, and man-machine interaction. It is widely used in recommendation, decision-making, question-answering, search, and other fields. However, previous studies mainly used low-level knowledge in the KG for reasoning, which may result in insufficient generalization and poor robustness of reasoning. To this end, this paper proposes a new inference approach using a novel knowledge augmentation strategy to improve the generalization capability of KG. This framework extracts high-level pyramidal knowledge from low-level knowledge and applies it to reasoning in a multi-level hierarchical KG, called knowledge pyramid in this paper. We tested some medical data sets using the proposed approach, and the experimental results show that the proposed knowledge pyramid has improved the knowledge inference performance with better generalization. Especially, when there are fewer training samples, the inference accuracy can be significantly improved.

翻译：基于知识图谱（KG）的推理一直被视为分析语义网络的有效手段，在信息检索、推荐、决策与人机交互等领域具有重要价值，广泛应用于推荐、决策、问答及搜索等场景。然而，现有研究主要利用知识图谱中的低层次知识进行推理，这可能导致推理泛化能力不足且鲁棒性较差。为此，本文提出一种基于新型知识增强策略的推理方法，以提升知识图谱的泛化能力。该框架从低层次知识中提取高层次金字塔知识，并将其应用于多层次层次化知识图谱的推理中——本文称之为知识金字塔。我们采用所提方法对部分医学数据集进行了测试，实验结果表明，所提出的知识金字塔在提升知识推理性能的同时表现出更优的泛化能力。特别是在训练样本较少时，推理准确率可获得显著提升。