## 4 The DIK Architecture
### Implementation
#### 4.3.2 Two-Step Data Fusion Implementation  
Medical knowledge fusion is founded on medical data extraction, and how to eliminate uncertainty in knowledge understanding, discover the true value of knowledge, and expand the correct knowledge update to the knowledge base is the focus of attention in medical knowledge fusion research [17].  
The key techniques of medical knowledge fusion contain an entity alignment technique, entity linking technique, and relationship deduction technique. Entity alignment techniques are used to eliminate the heterogeneity of ontologies and data sources, entity linking is the basis of medical knowledge fusion, and inconsistencies in knowledge are eliminated by operations such as entity disambiguation; the relational inference is used to discover implicit knowledge, thus expanding and complementing the medical knowledge base.  
The specific implementation of the two-step data fusion strategy is as follows (as shown in Figure 6):  
1. The first step is the data processing operation. The processing objects were for entity-type data that were obtained from different data sources using different methods. The data were processed using entity alignment methods to eliminate redundancy, remove errors, and perform alignment operations on entity terms with the same meaning and different names extracted from the data sources. When there was an entity alignment conflict, the entity in the medical data source was used as the standard, avoiding the incorrect operation of fusing similar disease entities into the same entity;
2. The second step is the fusion operation of medical data characteristics. In medical data, the role of disease entities was more central than that of various entities such as symptoms, treatments, drugs, departments, etc. Therefore, this paper utilized the characteristics of the \"disease hub\" to fuse medical entities from different data sources according to the developed medical relationship type, oriented by the names of disease entities. For example, the disease entity \"tuberculosis\" in the electronic medical record is firstly combined with the \"A15.001\" data in the medical dictionary through the \"disease code\" relationship type to form triplet data. Then, the relationship types of \"symptons\", \"department\", \"examination\" and \"treatment\" are combined with the corresponding data in the electronic medical record. Finally, the relationship types of \"food\" and \"medicine\" are combined with the corresponding data in the medical community.  
In addition, the entity alignment results are shown in Table 4, which count the operation results of the two methods for entity fusion. The difference in the number of disease entities between the method in this paper and the conventional method could directly indicate that the method used in this paper has improved the fusion of disease entities. In addition, the difference in the total number of medical entities between the two methods was equal to the difference in disease entities, which indicated that the method in this paper could effectively avoid the wrong operation of fusing different disease entities without losing the number of medical entities other than diseases.  
Figure 6: Two-Step Data Fusion Strategy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Disease Entities** & **Number of Medical Entities** \\ \hline Solid Alignment (General method) & 1275 & 39,359 \\ Entity Alignment (This paper’s method) & 1912 & 41,271 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of the results of the two entity alignment methods.