# Product Improvement Using Knowledge Mining and Effect Analogy
## 3 Proposed Method
### Retrieval of the Effect Knowledge  
With the help of the close relationship established by the LT-PES model, the effect retrieval mechanism based on the LT dimension is formed, as shown in Figure 4, using the following five steps. (1) The LT dimension is input into the effect knowledge base. The LT dimension comes from the NLP extraction result of the root cause of the problem. Because of the abstraction of the LT dimension, one LT dimension mostly corresponds to multiple physical quantities for query expansion [82]. (2) Constraints are applied for more applicable results. It also reduces the interference of irrelevant content. Specific constraints come from the problem part, effect part, and structure part of the model, such as design constraints, industry, and available resources. (3) The retrieval link obtains the qualified effect information from the established effect knowledge base. (4) The number of retrieval results is decided. Effect knowledge can be treated as an information block for processing, following [PERSON]'s law in product design, which limits the number of effect searches from five to nine. A small number is not enough to support the divergence of design thinking. We can gradually reduce restrictions and repeat the retrieval steps until the regulations are met. When the quantity is too large, the physical quantity parameters involved can be further defined in limiting conditions. Retrieval steps can also be repeated. (5) The math algorithm is used to rank alternative results. Finally, one or more effects with the highest availability are the design output. Ranking retrieval results is also a research branch of knowledge retrieval. Different from products or schemes with a complete structure, effect mainly provides abstract knowledge information. At present, effect ranking is still a complex and difficult process. Although the decision-making process relying on experts from internal or external consulting companies is common, it is highly subjective. Therefore, it is necessary to introduce mathematical tools to increase the reliability of ranking results. In the process of ranking the effects, experts provide an opinion range. Therefore, fuzzy numbers express expert views rather than exact numbers, and, in particular, triangular fuzzy numbers are most widely used [83]. A triangular fuzzy number effect ranking method based on fuzzy ordered weighted averaging (FOWA) [84] is formed. The method includes the following five steps to retain the advantages of paired comparison assignment and reduce information loss in the concept score matrix [85], coefficient of variation [86], and Dempster combination rule [87]. Step 1: Assuming that retrieval results of effects are n, indexes i and j have the same value range 1, 2, \(\ldots\), n. Through the pairwise comparison of these effects, a complementary evaluation matrix \(\widetilde{A}=\left(\widetilde{a_{ij}}\right)_{n\times n}\) composed of triangular fuzzy numbers is formed. The value of each element \(\widetilde{a}_{ij}=\left(a_{ij}^{L},a_{ij}^{M},a_{ij}^{Ll}\right)\) in the matrix \(\widetilde{A}\) is the common opinion of the expert group, and \(a_{ij}^{L}+a_{ii}^{L}=a_{ij}^{M}+a_{ii}^{M}=a_{ij}^{U}+a_{ii}^{L}=1\), \(a_{ii}^{L}=a_{ii}^{M}=a_{ii}^{U}=0.5\), and \(a_{ij}^{U}\geq a_{ij}^{M}\geq a_{ij}^{L}\) are specified. Step 2: Selecting appropriate fuzzy semantic quantization criteria to obtain the value of parameter pair (\(a\), \(b\)) in the corresponding operator \(Q\). There are three common criteria, namely, maximum (0.3, 0.8), at least half (0, 0.5), and as much as possible (0.5, 1). r is an intermediate parameter whose relative value is determined by \(j/n\) and (\(j-1\))/n in Equation (1). The weighting vector \(\omega_{j}\) can be obtained by Equations (1) and (2) [88]. Step 3: Deciding risk parameters \(\lambda\)\(\in\) [0, 1]. The more conservative the experts are, the smaller the value of \(\lambda\). Each element \(\widetilde{a}_{ij}^{(\lambda)}\) in the expected value matrix \(\widetilde{A}^{(\lambda)}\) is calculated by Equation (3).