# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 3 Proposed Name Disambiguation Scheme
### Clustering  
To distinguish between individuals with the same name, documents in the pool of potential matches must be clustered by unique authors. In the clustering stage, the similarity values generated from the previous name disambiguation analyzer were used. The similarity values were converted into distance values to be used as input data for cluster analysis. In this section, discerning between individuals with the same name using both rule-based and deep learning schemes of the name disambiguation analyzer is discussed. Using the similarity between documents of authors with the same name, determining whether the author of a paper being compared is indeed the same author is necessary. First, to convert inter-document similarity into a distance value used in HAC, a distance matrix transformation was performed. Using the converted distance values, HAC was executed to distinguish between individuals with the same name. HAC used in the name disambiguation scheme compared distances between clusters to perform clustering. For this, the document similarity generated by the name disambiguation analyzer was converted into a distance value. The inter-document similarity was represented in the form of a similarity matrix. The process of converting the similarity matrix into the distance matrix using the distance conversion formula, sim2 diss, is explained next. The similarity matrix is symmetrical in nature; hence, N \((\text{N}-1)/2\) pairs of a,b were generated. The distance value was computed for all pairs, and the results were organized in matrix form. Since distance values were calculated for every a,b pair, the matrix was a square matrix. The similarity values ranged between zero and four, based on a weighted application rule with four as the maximum score. The diagonal terms of the matrix were \"0\", as they represented the distance to oneself, making the matrix symmetrical about its diagonal. Regarding the size of the similarity matrix: in case of \(d\) documents, it compared all documents of authors with the same name to generate inter-document similarities, representing them in a \(d\times d\) matrix. Figure 6 illustrates the method of constructing the similarity matrix using inter-document similarities. The input data A, B, and C have a total of three distance values, and the distances between all pairs, such as A-B and A-C, can be represented as a 3 \(\times\) 3 matrix. Figure 5 displays a graph containing inter-document similarity values calculated from the name disambiguation analyzer. The distance between input data A and B is represented as one, A and C as three, and B and C as two. Figure 6 presents the similarity matrix in matrix form and shows all distance values from the graph data. Figure 5: Triplet network. (**a**) Triplet network, (**b**) Pid triplet. An example of representing inter-document similarity values in the form of a similarity matrix is listed in Table 4. Each row and column, such as Jang_0, Jang_1, Jang_2, and Jang_3, represents candidate documents with the same name. As every document is compared 1:1 for similarity, it forms a symmetrical matrix. Each element in the symmetrical matrix signifies the similarity values between the compared documents. Documents Jang_0 and Jang_1 exhibited a similarity value of four; therefore, both the documents were concluded to have been written by the same author. HAC calculates the distance between clusters and performs clustering based on these distance values. Therefore, in this study, we converted similarity values into inter-cluster distances. Equation (1) is a function provided in the statistical solution program R, which converts inter-document similarity into the inter-document distance value, known as the distance matrix, using the sim2 diss formula. \[1-(\frac{Similarity}{MAX}) \tag{1}\]  
The rule-based name disambiguation scheme represents the generated similarity as a distance value using rules based on document attribute values. Therefore, \(MAX\) in Equation (1) corresponds to the total number of attributes.