Document Understanding-Based Design Support: Application of Language Model for Design Knowledge Extraction

Design knowledge in the vast amount of design reports and documents can be an excellent resource for designers in their practice. However, capturing such domain-specific information embedded in long-length unstructured texts is always time-consuming and sometimes tricky. Therefore, it is highly desirable for a computer system to automatically extract the main knowledge points and their corresponding inner structures from given documents. In this study of document understanding for design support (DocUDS), a design-perspective knowledge extraction approach is proposed that uses phrase-level domain-specific labeled datasets to finetune a Bidirectional Encoder Representation from Transformers (BERT) model so that it can extract design knowledge from documents. The BERT model finetuning attempts to blend in the domain-specific knowledge of well-recognized domain concepts and is based on the datasets generated from design reports. The model is utilized to map the captured sentences to the main design entities <reguirement>, <function>, and <solution>. In addition, this approach uncovers inner relationships among the sentences and constructs overall structures of documents to enhance understanding. The definitions of design perspectives, inter-perspective relations, and intra-perspective relations are introduced, which together capture the main design knowledge points and their relations and constitute an understanding of the design domain knowledge of a text. The case study results have demonstrated the proposed approach's effectiveness in understanding and extracting relevant design knowledge points.