## 2 Methodology
### Classification of GDSW Based on Machine Learning
#### 2.4.1 Classification Algorithm
SVM was still able to identify GDSW more precisely, which means the GDSW were identified with better robustness using the SVM method. Figure 6: ROC curves of KNN, LR, RF, and SVM on sample data after being filtered by multi-source semantic information. In the PR curve, as shown in Figure 7, the classification precision of SVM is higher than that of KNN, LR, and RF, while all four classification algorithms can precisely identify GDSW when recall is less than 0.6. When recall increases again, the precision of all four classification algorithms becomes a gradient downward trend. Combined with the ROC curve, as shown in Figure 8, the four classification algorithms have better classification performance on the datasets, and their AUROC values are all greater than 0.9. In other words, in this case, when the value of AUROC does not differ much, we usually select the classification algorithm with the highest classification precision as the method to identify GDSW, that is, SVM, which is also in line with the conclusion in Section 3.1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Algorithm** & **Precision** & **Recall** & **F1-score** \\ \hline KNN & 72.7\% & 71.7\% & 72.4\% \\ LR & 53.0\% & 81.8\% & 64.3\% \\ RF & 64.0\% & 72.7\% & 68.1\% \\ SVM & 84.2\% & 72.7\% & 78.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision, recall, and F1-score of KNN, LR, RF, and SVM on robust data after being filtered by multi-source semantic information. Figure 7: Precision–recall curves of KNN, LR, RF, and SVM on robust data after being filtered by multi-source semantic information. ## 4 Discussion and Conclusions

This paper presents a classification method of GDSW based on machine learning. We collected a total of 79,402 websites related to geospatial data and used the proposed method to identify GDSW. Before identification, we filtered the websites by multi-source semantic information and then took the filtered websites as the sample data of machine learning. We used four classification methods to classify and predict the sample data. First, the experiment compared the effects of using a multi-source semantic similarity filter. Then, we verified the robustness of the proposed method using the same method and different data sources. The results show that the data filtered with multi-source semantic information as the sample data can identify GDSW more precisely than the pre-processed sample data only. Among the four machine learning classification methods, SVM has the highest precision in identifying GDSW. In addition, the robustness and scalability of this research are demonstrated. The results of applying the methods proposed in the paper to different sample data show that, among the four classification methods, SVM also performs best in precisely identifying GDSW among the data filtered with multi-source semantic information as the sample data for machine learning, and the classifier has higher classification performance. There are several methodological implications in this study. First, compared with traditional methods, the identification of GDSW based on the machine learning method has significant advantages. To the best of our knowledge, there is no quick and precise method to identify GDSW at present. This method solves the problems of the existing time- and energy-consuming search based on search engine and manual assessment, and the problem that the semantic discovery is limited by existing ontology. Second, in the preparation of sample data for the precise identification of GDSW using machine learning methods, the

Figure 8: ROC curves of KNN, LR, RF, and SVM on robust data after being filtered by multi-source semantic information. feasibility and usefulness of using multi-source semantic information to filter samples for improving the classification precision are demonstrated. The advantages of four machine learning algorithms for the precise identification of GDSW have been testified; specifically, SVM has the highest precision in identifying GDSW.