# A Knowledge Graph-Based Implicit Requirement Mining Method in Personalized Product Development
## 7 Conclusions  
The Internet's rapid development has led to a surge in the requirement for personalized product design and development. However, meeting implicit requirements that drive product innovation remains a challenge. To address this, this study proposed a method for mining personalized implicit requirements based on a domain knowledge graph. This method involves analyzing personalized requirements and knowledge from domain patents. A patent knowledge ontology layer representation method was proposed,  
Figure 19: Training performance of the structural entity classification model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Problems**} & \multicolumn{6}{c}{**Pre-Training Model**} \\ \cline{2-9}  & \multicolumn{2}{c}{**BERT-Base**} & \multicolumn{2}{c}{**BERT-wwm-ext**} & \multicolumn{2}{c}{**BERT-Base-Patent-Fusion**} & \multicolumn{2}{c}{**BERT-wwm-ext-Patent-Fusion**} \\ \cline{2-9}  & **Round** & **F-Score** & **Round** & **F-Score** & **Round** & **F-Score** & **Round** & **F-Score** \\ \hline
**Domain Patent Classification** & 6 & \(87.71\pm 6.41\%\) & 6 & \(89.06\pm 8.69\%\) & 6 & \(91.61\pm 6.54\%\) & 6 & \(92.56\pm 7.30\%\) \\
**Functional Entity** & 8 & \(65.42\pm 3.39\%\) & 9 & \(65.65\pm 3.54\%\) & 7 & \(65.39\pm 2.42\%\) & 10 & \(67.58\pm 2.92\%\) \\
**Structural Entity** & 7 & \(94.28\pm 3.59\%\) & 9 & \(94.38\pm 2.68\%\) & 10 & \(94.48\pm 3.49\%\) & 8 & \(94.64\pm 2.20\%\) \\
**Structural Entity Classification** & 4 & \(74.40\pm 4.26\%\) & 9 & \(73.13\pm 3.66\%\) & 5 & \(76.88\pm 3.80\%\) & 9 & \(76.60\pm 2.54\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of each pre-trained model on each problem of knowledge graph construction. leading to the construction of a domain knowledge graph. This expands the information contained in the knowledge graph. The two NLP models proposed during the study successfully improve the efficiency of graph construction. Two implicit requirement mining methods are proposed: one based on structural similarity and the other based on link prediction. These methods aim to overcome subjective professional limitations and broaden the requirement space for product innovation design. The method we propose and the platform we develop have good application prospects in the following contexts: First, in the context of generative design driven by large language models, it can be applied to prompt engineering, mining real and deep requirements that users find difficult to express, improving the model's understanding and empathy abilities, and further enhancing the feasibility and innovation of product development. Second, in the context of crowd innovation, it is possible to break through the subjective limitations of user requirements, improve communication efficiency between users and designers, reduce design iterations, and accelerate innovation convergence. However, this study has identified some limitations that require further exploration. On the one hand, the requirement expression paradigm represents the functions a product or component should achieve under what context. The requirement analysis method described in this paper realizes the extraction and mining of product functional requirements and structural requirements, but the requirement context is not considered enough. On the other hand, the study selects the patent as a field of knowledge network data and the knowledge source; in actual engineering applications, different enterprises have different types of databases and knowledge bases, and have less storage and a limited technical field. The research method proposed in the application of this kind of knowledge base effect may thus have certain limitations. Future research could focus on combining the usage context during personalized requirement analysis.