## 2 Methodology
### Data Filtering Based on Multi-Source Semantic Information  
The pre-processed websites contain a large number of blogs, companies, institutions, etc., which are irrelevant with geospatial data. The main reason is that the search engine performs fuzzy matching according to the input keywords and returns all the contents that contain full or partial keywords, which leads to a large number of non-geospatial data websites in the search results. Therefore, when we use machine learning methods to learn the features of geospatial data websites and non-geospatial data websites, the difference between the features of the two cannot be fully learned, which will affect the classification precision. Therefore, before using the machine learning method to precisely identify GDSW, we first filtered the pre-processed website list with semantic information. The similarity calculation based on multi-source semantic information mainly has three steps, as shown in Figure 4. (1) Establishment of multi-source semantic information. Geospatial data semantics and website feature semantics were constructed. The geospatialdata semantics included morphological semantics, source semantics, and content semantics. (2) Generation of word vector. Before the similarity calculation, both website text and multi-source semantic information needed to be represented in vectors. In this study, a word2 vec neural network model was trained to obtain a vector representation of words. (3) Similarity calculation. The similarity calculation included two parts, as shown in Part I and Part II in Figure 4. First, the semantic similarity between website theme and website feature semantics was calculated. If the similarity value was greater than a certain threshold, it is considered a potential geospatial data website. If the similarity value could not be judged, the comprehensive similarity between website and geospatial data content ontology (GDCO), geospatial data morphology ontology (GDMO), and geospatial data source ontology (GDSO) were calculated, that is, Part II in Figure 4, and the comprehensive similarity was used to judge again. Multi-source semantic information contains geospatial data content and geospatial data website characteristics. If a website is a geospatial data website, it must have metadata information of geospatial data and certain descriptive information, such as a data platform and sharing center. Therefore, the semantic characteristics of these two features can effectively filter non-geospatial data websites. As a formal expression of semantics, ontology plays an important role in semantic computing. The morphological ontology of geospatial data is a formal specification description of the conceptual system of morphological characteristics of geospatial data sharing, which contains descriptive information such as data type, data structure, data format, and storage medium. The source ontology of geospatial data is a formal specification of the source concept of geospatial data sharing, containing descriptive information such as data collection and data processing. Both morphological ontology and source ontology use existing achievements [39; 40]. A geospatial data content ontology (assumed as O) was constructed and divided into two categories at the conceptual level, including basic geographic elements and thematic elements. Ontology O was established using protege [21], an open-source software, and mainly used for knowledge-based ontology construction and ontology application in the semantic web. The content ontology of geospatial data includes 624 concepts and 470 individuals. Note that all the information contained in ontology in this study will be used as keywords to calculate similarity with websites. Figure 4: Flow chart of website filtering based on multi-source semantic information. The semantics of website features mainly measure the degree of semantic similarity of the website theme text (i.e., theme information in Section 2.2.2).