## 2 Related Work  
### Design Rationale Representations  
[PERSON] and [PERSON] [36] believe that scientific issues are \"tame\" problems, but that problems of planning cannot be meaningfully correct or false, and are \"wicked\" problems. On the basis of this theory, an issue-based information system (IBIS) model was proposed to address the co-ordination and planning of political decision-making processes [37]. The design is seen as a process of argumentation instead of a sequence of understanding the problem, analysis, synthesis, and implementation. Based on the IBIS model, gIBIS [27], DRL ontology [26], QQC [38], PHI, and Kuaba ontology [39] were proposed as DR representations. The DRed was developed as a DR tool for designers to record their rationale as the design proceeds. Issues, options, and associated pro and con arguments could be captured by the DRed in the form of a directed graph of dependencies [3]. These argumentation-based DR models are mostly used in the early conceptual design stage. [PERSON] et al. [34] proposed an ISAL model for DR extraction from technical documents, and [PERSON] et al. [35] proposed an ISAA model to build a semantic design knowledge network.  
[PERSON] [22] considers the design system as a knowledge-intensive system, and put forward the concept of a design knowledge system. [PERSON] classified design knowledge into tacit knowledge and codified knowledge. Tacit knowledge resides in a human brain, which is not easily codified or captured. Furthermore, design knowledge can also be divided into design process knowledge and design object knowledge. Design process knowledge describes how, whereas design object knowledge describes what. Design is a cognitive process of designers, which is a stepwise transformation process from the function space to the attribute space [40]. On this basis, [PERSON] [40] put forward the concept of design-intent modeling to record the design decision processes. [PERSON] and [PERSON] [41] proposed an intention-driven DR evolution model to model the cognitive design process. To achieve design collaboration, [PERSON] and [PERSON] [42] proposed a functional feature-based CAD modeling method, and [PERSON] et al. [43] proposed a feature semantic network for communicating design intent between heterogeneous CAD systems.  
Table 1 is a brief list of the DR representations.  
\begin{table}
\begin{tabular}{c c c} \hline
**Literature** & **DR Representation** & **Application Field** \\ \hline
[37] & IBIS: issue, position, argument (support or oppose) & political decision processes; \\  & DRL: decision problem, alternative, & general design \\
[44] & goal, argument, decision, viewpoint, & general design \\  & question, DRL relations & \\ \hline
[38] & QQC: questions, options, and criteria & human–computer interaction \\ \hline
[39] & Kuaba: question, idea, reasoning & \\  & element, artifact, decision, & model-based designs, software design \\  & argument, justification & \\ \hline
[41] & intent, option, justification, & \\  & decision, operation & engineering design \\ \hline \end{tabular}
\end{table}
Table 1: DR representations list.  
These studies gradually formed the DR theory: DR is strategic tacit knowledge, and in different application scenarios, there are some common constituent elements of DR. These studies are of great value to knowledge management. However, there is still a gap between the DR knowledge contained in the massive technical documentation and how to achieve the acquisition, sharing, and understanding of DR knowledge. Most of these studies focused on DR capture during the design process. Only the ISAL model is a DR representation for engineering design knowledge extraction from the documents. However, the most important information is not included in the ISAL model, such as the _pros_ and the _cons_. In addition, the extracted DR is mostly paragraphs which are not intuitive for designers as inspirational stimuli.