# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 3 Proposed Name Disambiguation Scheme
### Multi-Classifier  
In this study, name disambiguation was conducted by collecting data in real time from academic search websites. Metadata provided by heterogeneous academic search sites vary depending on the site characteristics. Therefore, name disambiguation needs to consider these varying characteristics. Not all academic search sites hold the metadata required for name disambiguation. Hence, conducting name disambiguation using the available metadata from these academic search services was necessary.  
The name disambiguation method proposed in this paper employed both rule-based and deep learning schemes. The rule-based approach has the advantage of quickly disambiguating names when documents with the applicable metadata are input based on set rules. Conversely, although the deep learning scheme requires training time, it can perform name disambiguation even in the absence of essential metadata. Therefore, even with the same metadata, diverse name disambiguation schemes can be applied to obtain results. In this paper, a multi-classifier that can select the appropriate name disambiguation scheme using the collected metadata is proposed. Using the multi-classifier, even if missing data  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[PERSON]\_0** & **[PERSON]\_1** & **[PERSON]\_2** & **[PERSON]\_3** \\ \hline [PERSON]\_0 & 1 & 0 & 0.525 & 0.85 \\ [PERSON]\_1 & 0 & 1 & 0.35 & 0.8 \\ [PERSON]\_2 & 0.525 & 0.35 & 1 & 0.85 \\ [PERSON]\_3 & 0.85 & 0.8 & 0.85 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Distance matrix example.  
Figure 7: Example of a dendrogram.  
are input, the appropriate name disambiguation scheme can be chosen to derive results. This method is scalable, i.e., it can allow future addition of new academic search services or new name disambiguation classifiers based on the results from the multi-classifier.  
Figure 8 displays the schematic of a multi-classifier that considers metadata from various academic search services and accordingly selects the appropriate name disambiguation method. When embedded name disambiguation data from name disambiguation candidates are input, the proposed multi-classifier selects between the presented rule-based and learning name disambiguation, considering the presence or absence of input metadata attributes.  
The multi-classifier operated through the following steps. First, we perform the feature embedding. The attributes of candidate documents for name disambiguation are transformed into vector representations via a feature embedding process, such as word2 vec, doc2 vec, and FastText. Next, we conduct performance evaluations for each name disambiguation schemes by using the transformed feature value. Finally, by comparing the performances, we select the best scheme as the ground truth label for the transformed feature value. We employ the labels to train the proposed multi-classifier. After all the ground truth labels were generated, training was conducted using these data and various existing multi-classifiers. The trained multi-classifier then received input values that were converted to vectors through the feature embedding process of site-specific metadata. Ultimately, the multi-classifier produced the most appropriate identification scheme as its output. Thus, selecting the most suitable name disambiguation scheme in real-world environments becomes possible where various metadata are generated. Furthermore, even when a new identification scheme is introduced, the multi-classifier can be extended by adding one label, enabling the utilization of an expandable multi-classifier model.