# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 3 Proposed Name Disambiguation Scheme
### Data Collector  
This study aimed to collect documents from heterogeneous academic search services, including research papers, as well as national R and D, patent, and research reports. Since heterogeneous forms of documents were collected from heterogeneous sites, understanding the metadata necessary for name disambiguation was crucial. The collector, upon user keyword input, collects the attribute information of documents appearing in the keyword search results and stores it in the internal database. The collected data are used as attributes for analysis in the name disambiguation analyzer after preprocessing in the next preprocessing step. When collecting the material, understanding the metadata used by the heterogeneous academic search services providing research outcomes is essential. For instance, academic search services, primarily offering papers, contain metadata distinguishing between academic journals and conferences. However, sites that primarily offer project information do not have such distinguishing metadata. Among them, whether they support metadata to identify international journal listings also varies. Moreover, domestic academic search sites that use journal information as metadata express it in various forms, such as journals, academic journals, and proceedings, which should be considered during data collection. We use type information about an article that the academic document cites, such as ScienceOn, KCI, NTIS and DBpia provide, to distinguish journals, academic journals, and proceedings. Next, understanding the attributes of various types of documents is essential. In academic search services, a significant proportion of authors publishing papers are affiliated with universities. However, for sites providing R and D information, authors publishing research outputs have various affiliations, including research institutes, national departments, companies, and universities. Furthermore, the authors of papers can be categorized into the main, co-, and corresponding authors. However, for R and D, the authors consist of participating researchers and research leaders. Thus, depending on the type of research output, the nature and form of attributes differ. Understanding the meaning of similar attributes and collecting them accordingly are crucial steps. The collector gathers all data usable for name disambiguation schemes. The research outputs of an author stored in the database after collecting the necessary values from academic search sites are listed in Table 1. All metadata that can be collected from academic search services are gathered. Among the collected metadata, the commonly used attributes and attributes for which advantageous weights can be given for name disambiguation are identified. Commonly usable metadata include the author name and affiliation, co-authors, title of the document, document keywords, and publication year. The attributes that can be given favorable weights for name disambiguation include the research field, email, academic journals, and academic conferences. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Paper 1** & **Paper 2** \\ \hline title & An Author Name Disambiguation Method Considering & \\  & Metadata Features & \\  & A same-name identification scheme that considers & \\ abstract & metadata to identify people with the same name on & \\  & heterogeneous sites... & \\ Keywords & Name disambiguation, Metadata & \\ Year & 2018 & \\ Affiliation & Chungbuk National University & \\ First Author & [PERSON] & \\ Co-author & [PERSON], [PERSON], [PERSON], [PERSON] & \\ Journal & Big data technology journal & \\ Publisher & Big Data Society & \\ e-mail(s) & [EMAIL_ADDRESS] & \\ Research area & bigdata & \\ Research Period & 2018–2020 & \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of collected attributes.