## 7 Conclusions  
In the medical field, with the gradual advancement of the medical informatization level, many medical resources have been accumulated, and the construction of the DIK medical knowledge graph with multiple data sources provides a method to extract knowledge from many medical resources, which has a broad application prospect. In this paper, firstly, according to the DIKW system and knowledge graph construction process, a set of DIK three-layer knowledge graph construction methods conforming to medical knowledge induction was proposed. Secondly, a data source selection strategy was proposed for the case of multiple data sources; again, a two-step data fusion strategy was developed for the characteristics of medical data to improve the effective fusion between different data sources. Finally, based on DIK medical knowledge graph, an intelligent medical question-and-answer system was built by combining the Flask framework and Python to realize the \"Natural Language Query \(\rightarrow\) DIK Medical Knowledge Graph \(\rightarrow\) Natural Language Answer\" process. To evaluate the effectiveness of the DIK medical knowledge graph, a set of simple comparative experiments was designed to verify it. The research approach proposed in this paper, taking into account the characteristics of the method itself and the data, had the purpose of designing a new data strategy and methodological model from a practical application perspective. This paper mainly discusses the application of DIK architecture in the medical field. However, the DIK architecture itself is a general knowledge framework that can be used for application implementation in various industries. Therefore, the method proposed in this paper still has some defects, shortcomings, and limitations, as shown below:  
1. Inaccuracy of entity correspondence. The main challenge in the medical knowledge fusion phase is to achieve accurate entity linkage. The possible causes are that the diversity of medical knowledge sources leads to serious multi-source referencing problems of medical entities in different data sources [30; 31; 32]. Therefore, how to link the extracted entities accurately and correctly to the medical knowledge base in a context-constrained manner is a common concern in the current academic community;
2. Storage method for the knowledge graph. The ternary representation is widely used and accepted. However, it suffers from problems such as low computational efficiency as the data volume grows. Therefore, representing the semantic information in medical entities as dense low-dimensional real-valued vector methods will be the next research direction [33; 34];
3. The scale of data is still not large enough, although the types of data in this study are diverse. For the DIK architecture to collect richer and more accurate information from the data, the data scale needs to be further expanded in future work [35; 36];
4. The imperfection of the architecture of DIK. Originally, the DIKW was a complete architecture, but due to the planning of the research work, the three-layer architecture of the DIK was adopted in this paper. In order to use the complete DIK architecture, it is necessary to consider how to proceed from the knowledge level to a higher level of abstracted knowledge [37]. **Author Contributions:** Conceptualization, M.L. ; methodology, M.L. ; validation, Z.N. ; investigation, Z.N. and L.T. ; writing--original draft, M.L. ; writing--review and editing, M.L., Z.N., L.T., Y.H., J.S. and Y.W. ; funding acquisition, L.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Key R&D Program of China (No. 2022 YFB2901304), and was funded by the Innovation Scientists and Technicians' Inoop Construction Projects of Henan Province (No. 224000510002), and was funded by Program of Songshan Laboratory (Included in the management of Major Science and Technology Program of Henan Province) (No. 221100210900-03), and (No. JCKY2018210B022).