# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 2 Related Work  
### Name Disambiguation  
Rule-based name disambiguation schemes extract distinguishable attributes of authors from papers and create rules using these attributes. Each rule incorporates a weight, which is determined dynamically according to performance evaluations, and these weights are applied in cluster analysis. In [7], a rule-based name disambiguation scheme was proposed. Figure 1 shows the flow chart of the rule-based name disambiguation scheme. When candidates with the same name input, a document vector is created based on the articles written by the candidates. Then, the rule-based heuristic similarity scores are calculated. Finally, they perform a clustering method, such as HAC, based on the similarity scores. Finally, they obtain dis-ambiguous data by generating clusters with the same authors. The collected data were preprocessed based on established rules. The preprocessed data were then subjected to name disambiguation using one of the two schemes: the rule-based name disambiguation method or a classifier-based method. After collecting documents from the database, attributes, such as surname, first name, co-authors, affiliation, research field, and keywords, were extracted for the name disambiguation scheme. The surname extracted during the preprocessing stage was used as is, while the initial of the first name was included in a data block, along with the attributes. Rule-based similarity was calculated on a block-by-block basis, and hierarchical agglomerative clustering (HAC) was performed based on the calculated similarity. HAC merges the most similar clusters together, starting with each data point as a separate cluster. HAC is most suitable in the author name disambiguation studies. For example, k-means clustering requires the number of cluster k. This is not suitable because the exact number of authors with the same name is unknown. The \"similarity estimated by classifiers\" method performed clustering based on similarity scores generated by classifiers and extracted stems from paper titles, abstracts, and keywords. For the extracted stems, similarity was calculated using the term frequency-inverse document frequency (TF-IDF) and latent semantic analysis models. The classifiers were trained using the information from data blocks. HAC was performed based on the similarity scores generated by the classifier. Name disambiguation was performed based on the results of the HAC. Figure 1: A flow chart of a rule-based name disambiguation scheme. A deep learning-based name disambiguation schemes generate weights using document attributes, converts these into vector values, and then proceeds with deep learning. The values obtained through learning are converted into inter-document distance values, after which cluster analysis is conducted to disambiguate names. The weights for deep learning represent document attributes in the form of graph data, extracting distinguishable attributes among multiple documents to create adjacency and feature matrices. This information is applied to a graph convolutional neural network (GCN) [23] to learn feature vectors. Name disambiguation is then performed based on the learned feature vectors using HAC. Figure 2 shows a flow chart of a deep learning-based name disambiguation scheme. Candidates with the same name enter as in the rule-based scheme. Then, it constructs graphs based on the papers written by the candidates. The constructed graphs are used in GCN. The papers of the candidates are also used to make feature vectors to represent the vertex features of the graphs. It performs GCN, which is a graph-based deep learning model, on the graphs by using feature vectors. Finally, the learned feature vectors are utilized to perform HAC. The papers written by the identical author are grouped as one cluster. A Graph Convolutional Network (GCN) is a specialized type of graph neural network designed to comprehend the connections between vertices in a graph data structure for tasks such as predicting associations or classifying vertices.