Patent-KG: Patent Knowledge Graph Use for Engineering Design
This work addresses the need for efficient knowledge extraction in engineering design, offering an incremental improvement by avoiding labeled data and complex rules while enhancing coverage and contextual information.
The paper tackles the problem of knowledge reuse in engineering design by building a patent-based knowledge graph (patent-KG) using an unsupervised mechanism that extracts knowledge facts from patents via attention graphs in language models, achieving a recall rate of 0.9 for mechanical engineering terms and extracting more relationship types including positional and negation relationships.
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.