# Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas
## 6 Experiment
### Results and Analysis  
#### 6.2.1 Coverage  
When calculating \(\mathbf{Cov}(\mathbf{Schema},\mathbf{Query})\), according to Formula (5), we need to calculate the weight of the entity types in Query. Then, the overlap degree of Schema over Query is calculated. The greater the total weight of the entity types that cover query, the higher the degree of overlap and the greater the result of \(\mathbf{Cov}(\mathbf{Schema},\mathbf{Query})\). The calculation method of \(\mathbf{Cov}(\mathbf{Schema},\mathbf{Dataset})\) is similar.  
As shown in Figure 8, when no entity types are removed, the coverage value calculated by **Method 2** is higher than that calculated by **Method 1**, because the importance of entity types not included in Schema but in Query is very small. This also shows that **Method 2** can measure the coverage more accurately. With the importance of removed entity types increasing from 1 to 3, the coverage value of **Method 2** falls faster and faster, because the absence of entity types with high importance in Schema will greatly reduce the overlapping effect between Schema and Query, while the decline of **Method 1** has no  
Figure 7: Number of entity types in different importance degrees.  
obvious change, indicating that the computation results of **Method 2** can better reflect the change in overlaps.  
In particular, when removing entity types with an importance degree of 4, the decreasing amplitude of **Method 2** becomes smaller, because there is only one entity type, _Patent_, with an importance degree of 4, and the weight of this entity type in Query is not high. Therefore, it presents abnormal points, as shown in Figure 8. When removing entity types with importance degrees of 5 and 6, the coverage value calculated by **Method 2** is lower than that calculated by **Method 1**, because if the most important entity types are not included in Schema, the overlap degree of Query to Schema will decrease greatly, while the calculated value of **Method 1** increases instead because it does not consider the difference in entity type weight.  
As for coverage of Schema to Datasets, the initial situation is like Query-Schema, as shown in Figure 9. However, when removing entity type with an importance of 4, the calculated values of the two methods both rise, which is because the entity type _Patent_ is not included in Datasets of this project, leading to an increase in the schema's overlap degree to Datasets after removing Patent from the schema. When removing entity types with the highest degrees of importance, the calculated value of **Method 2** drops significantly, because the weights of the most important entity types in Schema are also very high in Datasets. Removing these entity types will lead to a significant decrease in the overlap degree of Schema to Datasets, which is not reflected by the trend of **Method 1**.  
To sum up, when calculating the coverage of Schema to Query and Schema to Datasets, the calculation results of **Method 2**, which considers weight, can reflect the difference in importance of entity types better than **Method 1**.  
Figure 8: Coverage of Schema to Query.