# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 1 Introduction  
Generally, users enter specific keywords on academic search sites to find items through scholarly databases. These sites provide scholarly data, such as articles and reports, that match these keywords as search results. These search results include articles that provide information about the authors and contents of the articles. Since academic search sites hold several research records, individuals with the same name, even in the same research field, are commonly encountered. That is, researchers studying in the same or different fields often have identical names. Most academic search sites offer a feature to research within the author name search results. This feature is provided to refine and search again for the researcher or keyword the user actually wants to find. However, this feature poses a challenge, as users are required to determine for themselves from the search results if the name belongs to a different researcher. Additionally, even if a specific academic search site distinguishes between individuals with the same name effectively, determining those based on results provided by different academic search sites is very challenging. This is inconvenient and is a basis for incorrect judgments made by individuals searching for academic information. Therefore, in order to utilize various academic search sites, afunction that can identify individuals with the same name across these different academic search sites is necessary [1; 2]. Distinguishing and identifying individuals with the same name play a significant role in enhancing search accuracy. When users find the name of a researcher, the search results provide all the research outputs of every researcher with the same name. By using the filtering feature provided by academic search sites and entering additional information about the desired content, users can increase the accuracy of the search results. Studies on name disambiguation have been conducted using schemes that use the metadata of academic search sites to identify authors with identical surname and given name [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Various studies have been conducted to address name disambiguation on academic sites. In [5], a scheme was proposed to discern individuals with the same name using the metadata of academic search sites and determining name matches based on the similarity of attributes between two different papers. Furthermore, studies have been conducted to establish rules for calculating the similarity of attributes between two different papers and conduct cluster analysis based on the calculated similarity [6; 7]. A scheme that uses the metadata of a paper as a feature of deep neural networks has been proposed to discern individuals with the same name [10; 11; 12; 13; 14; 15]. A study in which individuals with the same name could be discerned by modeling a graph-based on the attributes of papers and author information and by using a graph auto-encoder was also conducted [17]. Some name disambiguation schemes have been studied [18; 19; 20]. Some of these proposed a taxonomy on the name disambiguation schemes, such as supervised learning, unsupervised learning, graph-based, semi-supervised, and heuristic-based, and also explored evidence- based schemes. They described the characteristics of each scheme, including its performance, strengths, and limitations. Others proposed initial-based methods based on bibliographic datasets in which the true identities of authors are known [21]. They insist that the first initial-based method already correctly identifies 97% of authors [22] and proposed an author name disambiguation framework by using knowledge graph embedding. The framework extracts entity features from a scholarly knowledge graph (SKG) to represent dense representation as low-dimensional vector space to perform HAC.