Patent Documents to Engineering Design Knowledge Graphs

Aimed at supporting knowledge-intensive tasks in the design process, populating design knowledge from text documents involves the extraction of triples - head entity :: relationship :: tail entity or h :: r :: t that could be combined into a knowledge graph representation. As relationships are largely chosen from ontological or common-sense alternatives, knowledge graphs built using these depict an approximation or restricted view of design knowledge, rather than what is explicated in text document. In this article, we present a data-driven approach to identify and explicate facts (h :: r :: t) from sentences in patent documents. We create a dataset of 44,227 sentences and facts, encompassing all patent classifications while also capturing the variations among patent document sections. Using this dataset, we train taggers that classify tokens to: 1) identify all entities (h) and relationships (r) and 2) specific relationships (r) for a pair of entities (h :: ___ :: t). While these taggers are built upon transformer-based sequence classification models, we evaluate our proposed method against edge classification approaches that use linear classifiers and graph neural networks, incorporating transformer-based token embeddings and linguistic features. The simplicity and coverage of the proposed method enable its application to patent documents at any scale and variety. Upon deploying an open-source python package, we apply our method to patent documents related to fan systems. From the knowledge graphs thus extracted, we explain how facts could be generalised to domain ontologies as well as be specified to subsystem levels. We also highlight the importance of knowledge graph representations by retrieving and explicating the knowledge of key issues in fan systems, while holding a comparative discussion against opinions from ChatGPT.