# Normalization of Web of Science Institution Names Based on Deep Learning
## 2 Related Works  
Based on the existing literature, scholars from both domestic and international contexts have conducted extensive theoretical and practical research on institution name synonymous recognition and standardization. In the field of synonym recognition for institution names, various methods have been employed. The representative methods include the following:  
1. String similarity-based methods: Common algorithms, such as the edit distance, Jaccard coefficient, and TF-IDF, are used to measure the similarity between institution names. The edit distance represents the minimum number of edit operations (insertion, deletion, or substitution) required to transform one string into another. [PERSON] [9] proposed the relative edit distance, which uses the edit distance divided by the minimum length of the two institution names to measure the similarity. To address syntactic variations in institution names, [PERSON] also introduced the word-based edit distance, which splits institution names into words and calculates the edit distance based on approximate word matching. 2. Statistical-based methods: These methods leverage the statistical characteristics of institution name occurrences, such as word frequency, co-occurrence relationships, and contextual features, to differentiate between different institutions. [PERSON] [22] assigned different weights to words based on their frequency and measured the similarity between two institution names by summing the weights of matching words. [PERSON] [16] proposed a clustering method using the Normalized Compression Distance (NCD) to match institution documents. The NCD utilizes data compression techniques to measure the similarity between two texts, assuming that if two texts are semantically similar, their compressed representations should exhibit high redundancy and similarity. [PERSON] [23] addressed naming ambiguities, spelling errors, OCR errors, abbreviations, and omissions by employing two strategies: one utilizing a Naive Bayes model when training data are available and the other employing a semi-supervised approach combining soft clustering and Bayesian learning when no learning resources are present. 3. Rule-based methods: These methods involve constructing rule libraries based on features derived from institution names (e.g., string similarity, substrings, word length, word order, and institution type) and additional features from the literature data (e.g., country, city, postal code, and author names) to merge institution name matches using feature-based rules. [PERSON] [5] proposed a rule-based and edit distance-based approach for institution name standardization. They first constructed an institution-author table and used the author, country, postal code, and other features for potential institution name matching. Then, they calculated similarity by combining the Jaccard word similarity, substring matching, and the edit distance to identify institution name variants. Researchers from Bielefeld University developed over 50,000 pattern matching rules utilizing features such as institution the name, start and end dates, URL, postal code, sectors (name, URL, and sub-classification), and relationships between institutions to disambiguate the author addresses in WOS and Scopus. 4. Entity linking-based methods: These methods resolve ambiguity by linking institution names in the literature to corresponding institutions in knowledge bases. [PERSON] [20] proposed the ELAD framework, which utilizes knowledge graphs for entity linking, generating a candidate set of institution entities, and then selecting the most probable institution entity based on string similarity. [PERSON] [19] introduced a framework that utilizes open data resources to assist institution name standardization and attribute enrichment. It involves normalizing institution names and enriching attributes using open data resources, constructing a data linking model for multidimensional attribute alignment, and proposing a dynamic management approach for open data. 5.