  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.
