## 2 Methodology
### Classification of GDSW Based on Machine Learning
#### 2.4.1 Classification Algorithm
The methods used in this study can precisely identify GDSW, but the following issues need to be further studied in the future: (1) the influence of keywords for search. Different keywords have various search results, and the searched data are critical in GDSW mining. In the next step, we will consider more keywords as input information for search engines. (2) The selection of an ontology library. Diverse ontology libraries will lead to different comprehensive similarity values and thus different sample data. We will consider the alignment of ontology libraries in the next step to enrich the ontology libraries. (3) Due to the different forms of geospatial data publishing, the method proposed in this paper has some limitations. For example, some websites not only provide geospatial datasets but also provide a large amount of information related to institutions, which may be filtered out when filtering with multi-source semantic information. Nevertheless, some proposed methods can be used for improvement, such as website key object extraction instead of extracting website keywords based on algorithms. (4) At present, only GDSW in Chinese have been identified in the study, and GDSW provided in English and other languages will be identified in the future. The purpose is to dig out the catalog of global GDSW and comprehensively evaluate the sharing degree and quality of global geospatial data. (5) In the future, we will explore the application of existing neural network models in GDSW classification, including convolutional neural networks (CNN) [53], and vector representation models for text, including bidirectional encoder representation from transformers (Bert) [54]. Conceptualization, Methodology, Validation, Formal Analysis, Q.C. and Y.Z. ; Software, Q.C., H.Z. and L.Q. ; supervision, J.S., S.W. and J.Z. ; Funding Acquisition, Y.Z. ; Writing--original draft preparation, Q.C. ; Writing--review and editing, Q.C., Y.Z. and Y.Q. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (grant numbers: 42050101, 41771430, 41631177) and the Strategic Priority Research Program of the Chinese Academy of Sciences, (grant number: XDA23100100). Not applicable. The data presented in this study are available in Section 2.2. The authors declare no conflict of interest. ## References

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