  To facilitate knowledge reuse in engineering design, several dataset
approaches have been proposed and applied by designers. This paper builds a
patent-based knowledge graph, patent-KG, to represent the knowledge facts in
patents for engineering design. The arising patent-KG approach proposes a new
unsupervised mechanism to extract knowledge facts in a patent, by searching the
attention graph in language models. This method avoids using expensive labelled
data in supervised learning or listing complex syntactic rules in rule-based
extraction. The extracted entities are compared with other benchmarks in the
criteria of recall rate. The result reaches the highest 0.9 recall rate in the
standard list of mechanical engineering related technical terms, which means
the highest coverage of engineering words. The extracted relationships are also
compared with other benchmarks. The result shows that our method provides more
contextual information in relationships, and extracts more relationship types
including positional and negation relationships.
