Deep Learning-Driven Innovative Model for Generating Functional Knowledge Units

Design science research shows that existing knowledge is the basis for product design. The functional knowledge unit is the most basic knowledge to describe the functional design knowledge. Nowadays, the acquisition of functional units is mainly manual, which is time-consuming and labor-intensive. Functional knowledge integration is an effective way to achieve innovation design, yet the insufficient functional units cannot effectively support the integration. To address the above issue, this paper proposes a named-entity recognition (NER) model called Boundary Perception NER (BP-NER). From the product manual, BP-NER can automatically extract information necessary to describe the functional unit. The model leverages entity boundary information to predict entity classification labels and incorporates semantically-rich character-level feature information. BP-NER also introduces FocalLoss function to solve the problem of label imbalance. Experiments on the functional unit dataset demonstrate the effectiveness of the proposed model. Compared with the baseline model BERT-BiLSTM-CRF, BP-NER increases the overall label prediction accuracy by 5.05%, and the average F1-score improvement is 32.8% for entities CIN, COT, DIN, DOT and ENY.