## 2 Methodology
### Data Filtering Based on Multi-Source Semantic Information
#### 2.3.2 Similarity Calculation  
1. Calculation method: The semantic similarity between words was quantified by the distance function. It was assumed that \(\overset{\rightarrow}{\vec{k}}=(\textit{k}_{1},\ \ldots,\textit{k}_{n})\) and \(\overset{\rightarrow}{\textit{k}^{\prime}}=(\textit{k}^{\prime}_{1},\ldots, \textit{k}^{\prime}_{n})\) are two-word vectors of website and multi-source semantic information, respectively. \(\overset{\rightarrow}{\textit{k}}\) and \(\overset{\rightarrow}{\textit{k}^{\prime}}\) use the cosine function of N-dimensional vector space to calculate their similarity, and the distance function should satisfy the following four axioms [42]: \(\bullet\)\(\quad f(x,y)\geq 0(non-negativity)\) \(\bullet\)\(\quad f(x,y)=0(identity)\) \(\bullet\)\(\quad f(x,y)=f(y,x)(symmetry)\) \(\bullet\)\(\quad f(x,y)\leq f(x,y)+f(y,z)(subadditivity)\) Here, the parameters \(x,y,z\) are members of n-dimensional vector space. The cosine distance is described as follows:  
\[d_{\textit{cos}}(\overset{\rightarrow}{\vec{k}},\overset{\rightarrow}{ \textit{k}^{\prime}})=\frac{\sum_{i=1}^{n}\left|\textit{k}_{i}\times\textit{ k}^{\prime}_{i}\right|}{\left\|\overset{\rightarrow}{\textit{k}}\right\|\times \left\|\overset{\rightarrow}{\textit{k}^{\prime}}\right\|} \tag{2}\]  
where \(\overset{\rightarrow}{\parallel\textit{k}\parallel}\) is \(\sqrt{\sum_{i=1}^{n}\left(\textit{k}_{i}\right)^{2}}\), and \(\left|\overset{\rightarrow}{\textit{k}^{\prime}}\right|\) is \(\sqrt{\sum_{i=1}^{n}\left(\textit{k}^{\prime}_{i}\right)^{2}}\). The similarity between words is calculated, and the similarity of word sets is calculated as follows: in word sets A and B, A has m words, namely A\({}_{1}\),..., A\({}_{m}\), where A\({}_{1}\) is the first word of A, and A\({}_{m}\) is the m-th word of A; similarly, B has n words, namely B\({}_{1}\),..., B\({}_{n}\), and B\({}_{n}\) is the n-th word of B; then, the similarity between A\({}_{m}\) and B\({}_{n}\) is \(Sim(\text{A}_{m},\ \text{B}_{n})\), and the similarity can be calculated for each word in A and each word in B. First, we compared A\({}_{1}\) with B\({}_{1}\) to B\({}_{n}\) to find the similarity value of the words with the largest similarity value, then compared A\({}_{2}\) with B\({}_{1}\) to B\({}_{n}\) to find the similarity value of the words with the highest similarity between A\({}_{2}\) and B. Finally, we summed these maximum similarity values, divided this sum by the average length of two-word sets, and obtained the sentence similarity between A and B. The formula is as follows [42]:  
\[Sim(\text{A},\text{B})=\frac{\sum_{i=1}^{m}\text{Max}(\text{Sim}(\text{A}_{i}, \text{ B}_{1}),\ \dots,\ \text{Sim}(\text{A}_{i},\ \text{B}_{n}))}{\text{L}},\ \text{L}=\text{L}(\text{A})+\frac{|\text{L}(\text{B})- \text{L}(\text{A})|}{2} \tag{3}\]  
In the formula, the length of A is L(A), and the length of B is L(B). According to the similarity calculation formula, we calculated the similarity between each website in L and the multi-source semantic information. First, we calculated the website features, that is, the semantic similarity between the theme of each URL and the preset website features. If the similarity was greater than 0.9, it was considered a potential geospatial data website and stored in W. When we could not determine whether it is a potential geospatial data website through the website features, the GDCO, GDMO, and GDSO were applied to calculate the data similarity with the website content, respectively. Finally, we obtained the comprehensive similarity (as in Equation (4)). If the comprehensive similarity was greater than 0.8, it was considered a potential geospatial data website and stored in W. The similarity thresholds of website features and data features were set to 0.9 and 0.8, respectively. Through the statistical analysis, clustering, and manual assessment, we found that, when the similarity thresholds of website features and data features were less than 0.9 and 0.8, respectively, the corresponding websites were almost non-geospatial data websites.