# A Knowledge Graph-Based Implicit Requirement Mining Method in Personalized Product Development
## 1 Introduction  
The field of engineering design, especially in product design, service system design, and complex system design, is being reshaped by two major trends: on the one hand, with the rise of large language models, generic-field generative intelligent design is gradually becoming mainstream [1]. On the other hand, driven by the Internet, human designers have formed new design models, such as crowdsourcing design and group intelligence innovative design, through collaborative organization [2; 3; 4]. The core goal of these development trends is to address the growing personalized requirements and achieve rapid iterative design of products and user-participation design. In engineering design, the expression of personalized requirements has significant semi-professional characteristics, which will be elaborated in detail in Section 3 \"Nature of the Problem\". This semi-professional feature provides feasibility for directly extracting requirement elements from user descriptions, but also increases the importance and difficulty of implicit requirement mining. In the field of generative intelligent design, a major challenge is how to make machines empathetic [5], so that after users input personalized requirements, they can empathize with the design and product usage context, automate the completion of users' prompts, mine design problems that users have not anticipated or are difficult to express clearly, and improve the output quality of intelligent generative models. In terms of group intelligence innovation design, there are limitations in users' understanding of products, which is not conducive to the emergence of innovation, butalso becomes a fundamental problem that leads to the design process being repetitive, chaotic, and of low design quality [2; 3; 4]. Therefore, accurately mining and supplementing implicit needs that have not been clearly expressed in personalized requirements is of great significance for improving the innovation of engineering design, the completeness of product functions, and the feasibility of solutions. Many studies have analyzed the design requirements of [6; 7; 8] through data-driven technologies such as machine learning. Although these studies extensively use data derived from user reviews on the Internet, user-generated content (UGC) has inherent limitations in complementing user needs [9; 10]. In addition, it is difficult for the review data in the field of engineering design to meet the research needs in terms of quantity and quality, and the timeliness of the data is not in line with the development trend of engineering design. To overcome these limitations, this study introduced the engineering design knowledge contained in the patents. This research presents a method to identify implicit personalized requirements using knowledge graphs. First, this study provides an in-depth analysis of semi-professional, task-oriented personalized requirements from the perspective of design science and engineering theory to reveal the characteristics of implicit requirements. Then, it builds a domain knowledge graph by gathering product design knowledge from patents. By optimizing the ontology layer and stacking the pre-training model, the comprehensiveness and practicability of the knowledge graph are enhanced while improving its construction efficiency. Finally, taking the user's personalized description as input, the explicit requirement elements are extracted and the corresponding knowledge entities are matched, and the mining of multiple personalized implicit requirements is realized based on the methods of structural similarity and link prediction. This study makes several key contributions:  
* It performs a detailed analysis of the user requirements of the front-end design stage, clarifies the characteristics, forms, and types of the personalized implicit requirements of users that drive product innovation, and enriches the theoretical basis of the related research of the implicit requirement mining for innovative design.