# Normalization of Web of Science Institution Names Based on Deep Learning
## 1 Introduction
To address the limitations of literal similarity, some researchers have combined author name features, address features (city/state/country names) [11], and institution name features (organizational keywords) [12; 13] with string similarity algorithms to achieve better results [14; 15]. Another group of researchers [16] introduced statistical approaches by applying the principles of TF-IDF and analyzing a large number of institution names. They found that high-frequency words had limited discriminative power for distinguishing institution entities. To overcome this, they assigned different weights to words in institution names based on their frequencies and used the weighted average of different words in addresses to determine whether they referred to the same institution. Some scholars have adopted entity linking methods in an attempt to link institution names to external knowledge bases. Initially, researchers used proprietary databases from governments or institutions [17]. However, these private databases were often small in scale and not publicly accessible, limited to disambiguating institutions within specific regions or fields. With the development of publicly available institution knowledge bases and big data technologies, recent studies have focused on constructing standardized models for institution names. These models link institution entities in bibliographic records to multiple-source institution identifiers [18; 19; 20], such as Wikidata, GRID, ISNI, Ringgold, ROR, etc. With the advancement of deep learning, the advantages of automatically learning features from limited annotated data have been widely applied in the field of entity dis \begin{table}
\begin{tabular}{c c c} \hline \hline
**Official Name** & **Variant Name** & **Description** \\ \hline \multirow{8}{*}{Soochow Univ} & Suzhou Univ & alias \\  & Soochow Univers & word abbreviation \\ \cline{1-1}  & SUDA & acronym \\ \cline{1-1}  & SoChow Univ & spelling errors \\ \cline{1-1}  & SoChow Univ & missing agency identifiers \\ \cline{1-1}  & University Soochow & syntactic arrangements \\ \cline{1-1}  & Soochow Univ affiliated Hosp & nested entities \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different expressions of the name of the institution. ambiguation. Currently, deep learning methods are less commonly used in institution disambiguation research on bibliographic data, with the predominant use of word vector-based approaches. These methods utilize word vector models such as Word2 Vec, GloVe, and BERT to learn the semantic relationships of institution names and combine clustering, rules, or string-based methods to identify the form similarity, variants, and abbreviations of institution names [21]. Currently, there are two main issues in institution name standardization using deep learning methods:  
1. Feature Extraction and Fusion: Institution data features can be categorized into two main types: text features and semantic relationship features. Text features primarily measure the literal similarity of institution names, which are effective in identifying names that are similar in their literal form. However, they may perform poorly in handling institution aliases and abbreviations. On the other hand, semantic relationship features focus on analyzing co-occurrence relationships and hierarchical similarity between institutions, which can better identify aliases and abbreviations. However, they may sometimes incorrectly merge structurally similar but distinct institutions. The current research often employs techniques such as term frequency, TF-IDF, string similarity, and the longest common substring to extract text features and deep learning models such as Word2 Vec to extract semantic features. These features are then combined through rules or weighted fusion. This approach separates the association between text and semantic features and introduces uncertainty and subjectivity in feature combination and fusion weight allocation. 2.