## 4 Empirical Study
### Comparison and Discussion  
To demonstrate the contributions of the proposed approach, we compared it with several typical state-of-the-art DR representations, as shown in Table 6. By comparison, the proposed approach has the following advantages: (1) It supports the classic DR theories. The DKSN representation is fully compatible with DR, based on design argumentation and design intent based on design thinking, and is more adaptable. DR is valuable because it is tacit knowledge about the process of design argumentation or design thinking. Most of the DR is lost, and only a small part of the knowledge is recorded in the documentation. Therefore, the extracted DR should be consistent with classical DR theory, and the most important information, such as _pros and cons_, and _alternatives_, should not be omitted. (2) It supports the NLP. The experimental results show that our method takes into consideration the linguistic features of the design literature, and is more suitable for sentence classification, sentiment analysis, information extraction, and other NLP technologies. For other studies on DR representation, only the ISAL model covers text mining. (3) It supports the construction of knowledge graphs. The construction of the design knowledge graphs would be beneficial to knowledge acquisition, sharing, and reuse. Based on the Semantic Web, ontology, and knowledge graph, a formalized ODKN could be established, which is suitable for the semantic retrieval and straightforward understanding of designers. The ODKN could accelerate the acquisition, sharing, and reuse of design knowledge. (4) It can provide designers with creative design stimuli. For innovative designers, DR should be presented in a short, easy-to-understand form. If the extracted DR is a long paragraph, it is not simple enough, and will hinder the designer's quick understanding. The DR extracted by our method is a DR sentence, phrase, or word, which meets this requirement. To illustrate the metric of our DKSN model, we intend to conceptually compare DKSN with ISAL. ISAL is the only representation model of DR which is intended for DR discovery from patent documents. Figure 9 shows an example of extracted knowledge from a patent document using our DKSN model through text mining. Table 7 illustrates \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Literature** & **Info of Argumentation** & **Support Text** & **Straightforward** & **Support Knowledge** \\
**Source** & **or Thinking** & **Mining** & **and Intuitive** & **Graph** \\ \hline
[35] & issue, solution, & & & \\  & artifact, argument & No & yes (term) & No \\  & issue, solution (usage, & & \\  & performance, accuracy, & & & \\  & product, material, & & & \\  & structure, function) & & & \\  & part, assembly, functional & & & \\  & requirement, geometry & No & N/A & No \\  & feature, constraint & & & \\  & artifact, design history, & & & \\  & participants, artifact & & & \\  & structure, boundary & & & \\  & representation & & & \\  & ISAL: issue, & & & \\  & solution, artifact & & & \\  & artifact, issue, intent, & & & \\  & pros and cons, & & & \\  & alternative, and relation & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons of DR representation in the field of engineering design. the DR of the same patent, captured manually via reference to the methods in [34; 72]. From the comparison, we can know that the extracted knowledge using DKSN could more accurately show the internal logical relationship of the extracted knowledge, and that it is more straightforward and intuitive, which is suitable for a designer as a kind of inspirational stimulus. **Issue:**  
Maintenance of infrastructure that includes fixtures or other equipment can be difficult, depending on the location of the equipment.... A service person's limited maneuverability when on a ladder increases the risk of falling or serious injury during a maintenance operation.