# Aging-Friendly Design Research: Knowledge Graph Construction for Elderly Advantage Applications
## 5 Discussion  
### Theoretical Contributions and Practical Implications  
In theoretical terms, we applied the combined KJ-AHP method to process the raw data in the graph, providing a structured and quantitative analytical approach in the considered \begin{table}
\begin{tabular}{c l l c} \hline \hline
**Serial No.** & **Question** & **Purpose** & **Satisfaction Mean** \\ \hline
1 & Is the operation process clear? & Process satisfaction & 3.9 \\ \hline
2 & Is it easy to understand and master? & Usability and learnability & 2.8 \\ \hline
3 & Is the feedback content accurate? & Data structure satisfaction & 4.0 \\ \hline
4 & Are the relationships among the & & \\  & contents clear? & Relationship mining satisfaction & 4.5 \\ \hline
5 & Does it enhance design creativity? & Creativity enhancement satisfaction & 3.8 \\ \hline
6 & Does it help guide the design thinking? & Improvement of thought process & 4.6 \\ \hline
7 & Has the graph fulfilled its intended & Satisfaction with graph functionality & 4.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Satisfaction questionnaire survey results. Figure 18: Functional verification of advantage application prioritization. field. Compared to other decision-making methods, such as TOPSIS and KANO, the AHP's multi-level structure clearly presents the dependencies between the levels, providing a clear framework for constructing a knowledge graph. Specifically, the levels in the AHP model framework align well with the core elements (e.g., labels, relationships, and entities) in the Neo4j database, and their effective mapping significantly simplifies the transition from the data layer to the knowledge layer. This study systematically demonstrates the application of the KJ-AHP method in graph data processing. The proposed mapping rules among the AHP levels, the graph ontology levels, and the database elements provide important references for related research. In practice, this study explored the application of elderly advantages in design practice from the perspective of age-friendly design. Through brainstorming sessions and workshops, the research team thoroughly discussed the difficulties and needs that designers encounter when applying elderly advantages in design.Based on this, we proposed a knowledge fusion approach centered on knowledge graph technology, promoting the integration of elderly advantages and age-friendly design elements. The research team validated the approach through practice and developed an elderly advantages knowledge graph for age-friendly design. The functionality test of the graph showed that the elderly advantages knowledge graph developed in this study can help designers with data structuring, clarifying data relationships, and expanding design ideas, ultimately improving the feasibility of design solutions. Specifically, the application of the KJ-AHP-KG method affects design decisions in the following three ways. Enhancing Data Completeness for Multi-dimensional Decision Analysis: As highlighted in the introduction, current case study methods face challenges in generating comprehensive and systematic data on elderly advantages. In contrast, the data stored in a knowledge graph can complement case study findings, thereby enhancing the completeness of the data. This supplementation ensures that the decision-making process takes into account a broader range of relevant factors. Improving the Structuring of Visual Data through AHP to Enhance Decision Accuracy: The hierarchical nature of the AHP method strengthens the structure of the ontology, making the overall data presentation more intuitive. By ranking the importance of various factors, the AHP helps prioritize core issues that should be addressed first in the decision-making process. Revealing Data Associations to Inform Decision-Making: Through the construction of the ontology, this study clarifies the relationships between different data types, such as users, pain points, behaviors, needs, and relevant advantages.