# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 3 Proposed Name Disambiguation Scheme
### Preprocessing  
Data collected directly from the collector cannot be used as they are; therefore, preprocessing is required. In the preprocessor, all documents containing the same author name are gathered as potential name disambiguation candidates. Attributes needed for name disambiguation analysis are then extracted from the documents. At this point, affiliation information is normalized using the affiliation table. From the collected documents, candidate groups for name disambiguation need to be generated to narrow down the set of documents that can be considered as potential name matches. Name disambiguation is performed using document similarity within the created candidate sets. In this study, two or more documents with the same author name are considered as candidate groups. Table 2 shows an example of a name disambiguation candidate group. Collected data containing two or more documents with the author name \"[PERSON]\" were generated as name disambiguation candidates. Within the name disambiguation candidate group, attributes, such as the title of the paper, affiliation, publication year, co-authors, journals, and academic conferences, were collected. To help explain the name disambiguation scheme proposed in this paper, documents \"[PERSON]_0\", \"[PERSON]_1\", and \"[PERSON]_2\" represented unique research outputs of a single [PERSON] author, while document \"[PERSON]_3\" represented a research output of a different [PERSON] with the same name. The proposed scheme constitutes the name disambiguation document candidate group from all research outputs with the same author name, regardless of the type of authorship (main author, co-author, or corresponding author). Each document in the name disambiguation candidate group was labeled with the author name, and numbers were appended after the name to distinguish documents. In this study, we normalized the affiliations listed in documents. The listing of affiliations (when the author affiliation is a university) can vary across academic search services. Furthermore, authors may have different styles of listing their affiliations. For example, an affiliation Pennsylvania State University, depending on different styles, can be written as Pennsylvania State University, Pennsylvania State Univ., PSU, Penn. State Univ., or Penn. State College, among other variations. Additionally, in some instances, such as \"Information Sciences and Technology, Penn. State Univ.\", a specific department or detailed affiliation information is included. For such cases, a standardized affiliation form needs to be normalized. Web of Science, a globally renowned academic search service, provides affiliation metadata to alleviate confusion caused by various affiliation entries and to verify various forms of affiliation information. Leveraging this, the same affiliation information \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Title** & **Inst.** & **Year** & **Co-Author** & **Journal** \\ \hline \multirow{3}{*}{[PERSON]\_0} & Author Name & \multirow{3}{*}{
\begin{tabular}{c} Chungk \\ National Univ. \\ \end{tabular} } & \multirow{3}{*}{2018} & ([PERSON], [PERSON], [PERSON], \\  & & & & [PERSON]. [PERSON], [PERSON]) \\ \cline{1-1}  & Metadata Learning by & & & \\ \cline{1-1}  & using Machine learning. & & & \\ \cline{1-1}  & [PERSON]’s Contribution to & & & \\ \cline{1-1}  & ERA. & & & \\ \cline{1-1}  & Fuel Cell system & & & \\ \cline{1-1}  & considering. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example of name disambiguation candidates. can be normalized. In our research, affiliations listed in various forms were normalized to a unified format. All institution names and their synonyms listed on academic search services were stored in a database. Based on the stored information, synonymous institution names were standardized into a representative institution name for affiliation notation. Affiliations with detailed information (e.g., departments) were converted into only the university name to efficiently process the affiliation information.