## 4 The DIK Architecture
### The Architecture of the DIK Three-Layer Knowledge Graph
#### 4.2.2 Information Layer  
The IG\({}_{\text{DIK}}\) represents the information layer knowledge graph and improves the co-hesiveness of IG\({}_{\text{DIK}}\) nodes by performing initial abstraction of concept mapping and relationship combination between the interaction degree and context mining of Data\({}_{\text{DIK}}\) entity nodes in DG\({}_{\text{DIK}}\) to obtain the Information\({}_{\text{DIK}}\) node [12]. In DG\({}_{\text{DIK}}\), only the data frequency was counted, and the accuracy of the acquired data was not analyzed, so a large amount of redundant data was generated. Therefore, in the IG\({}_{\text{DIK}}\) layer, data cleaning was performed to eliminate redundant data, and preliminary abstraction was performed to improve the cohesiveness of the data based on the interaction degree between entity nodes. To be more integrated with the real situation in healthcare and to improve the significance of statistical data, for the recording of the interaction frequency between entity nodes on IG\({}_{\text{DIK}}\), we only consider the direction of interaction between entities and not the type of interaction. Because different nodes may have the same interaction type or a wide variety of interaction types (reducing statistical significance), but the in-degree and out-degree of each node can be precisely quantified statistically. In order to reduce redundant data, we defined the composite degree to measure the importance of nodes in IG\({}_{\text{DIK}}\), and the calculation formula is defined as follows:  
\[\text{Com\_degree}=\sqrt{\text{deg}^{+}\times\text{deg}^{-}} \tag{4}\]  
Figure 3: Statistics of Data\({}_{\text{DIK}}\) on f\({}_{\text{spatial}}\) and f\({}_{\text{structure}}\). As shown in Equation (4), deg\({}^{+}\) and deg\({}^{-}\) are the in-degree and out-degree of the node, respectively. However, the composite degree on IG\({}_{\text{DIK}}\) only counts the interaction frequency of nodes, and there is no improvement compared with the frequency statistics in DG\({}_{\text{DIK}}\). As shown in Figure 4, Entity1 and Entity2 are low-frequency nodes on DG\({}_{\text{DIK}}\), and Entity3 and Entity4 are high-frequency nodes on DG\({}_{\text{DIK}}\), i.e., \(\text{DFreq}=(\text{Entity1},\text{Entity2})\)\(<\text{DFreq}=(\text{Entity3},\text{Entity4})\). However, the Com_degree on IG\({}_{\text{DIK}}\) is the opposite result, i.e., Com_degree \(=(\text{Entity1},\text{Entity2})>\text{Com\_degree}=(\text{Entity3},\text{Entity4})\). Therefore, only using the composite degree is used to measure the nodes in IG\({}_{\text{DIK}}\), which tends to lose information. To measure the importance of nodes further accurately in IG\({}_{\text{DIK}}\), we defined the calculation of Impor for retaining the information of nodes on DG\({}_{\text{DIK}}\). where \(\alpha\) and \(\beta\) denote the weight coefficients of the DFreq of entity nodes on DG\({}_{\text{DIK}}\) and Com_degree on IG\({}_{\text{DIK}}\), respectively, for measuring the importance of nodes, both of which can be obtained by simple learning model training. \[\text{Impor}=\alpha\text{DFreq}\times\beta\text{Com\_degree} \tag{5}\]  
By mining the \(\text{Data}_{\text{DIK}}\) elements in DG\({}_{\text{DIK}}\), a new concept Information\({}_{\text{DIK}}\) was generated in IG\({}_{\text{DIK}}\), which reflected multiple interactions between nodes. For example, the \(\text{Data}_{\text{DIK}}\) element \"pharyngitis, rhinitis, tonsillitis\", etc., is integrated to improve the expression of the medical knowledge graph, and the Information\({}_{\text{DIK}}\) element \"ENT disease\" is obtained. Therefore, we calculated the internal interaction degree and external interaction degree by circling a specific number of entities, as shown in Equation (6). \[\text{cohesion}=\frac{\text{IFreq}_{\text{II}}}{\text{IFreq}_{\text{EI}}} \tag{6}\]  
Figure 4: Measuring the importance of entity nodes in Information\({}_{\text{DIK}}\). Cohesion is the ratio of internal interaction to external interaction and is a measure of the degree of association between entity nodes.