# Coarse-to-Fine Entity Alignment for Chinese Heterogeneous Encyclopedia Knowledge Base
## 2 Related Work
### Similarity-Based Alignment  
From the perspective of information analysis methods, entity alignment methods could be classified as follows: based on network ontology semantics [31], based on rule analysis and based on similarity theory judgment. Entity alignment based on web ontology semantics uses the web ontology language (OWL) to describe the ontology and to perform reasoning and alignment. This type of method combines the semantic information of OWL in the knowledge base, such as the heuristic reasoning algorithm [32], iterative discriminant model [33], to reason and judge whether it can be aligned. However, for Chinese encyclopedia knowledge bases, such as Baidu Baike and Hudong Baike, do not contain complete ontology information, so it is difficult to align them according to OWL semantics. The entity alignment method based on rule analysis uses the rule-based evaluation function to judge and align by formulating special rules in specific application scenarios. However, the method based on rule analysis is not universal. It is possible to perform alignment based on rule analysis only by establishing a large number of different rules in many specific areas. Similarly, Chinese Web Encyclopedia contains a large amount of entity  
Figure 1: Bayesian Network for the LDA model. This diagram illustrates the process of generating documents. The symbol at the bottom right of the box represents the number of samples in the box, \(K\) represents the number of topics, \(N\) represents the number of words, and \(D\) represents the number of documents. neighborhood information, and it is difficult to align it by establishing a large number of different rules for specific fields. Therefore, in the face of Chinese encyclopedia knowledge bases that do not have complete ontology information and that contain a large number of subject areas, more research is focused on the method of similarity theory determination. The method of judging based on the similarity theory is widely used. This method considers the attribute value information or description text information of the entity, and calculates the similarity of such information for alignment. Some research has used similarity-based fuzzy matching, topic models, etc. to align the entities of the encyclopedia and knowledge base. Among the methods of attribute similarity calculation, there are mainly token-based similarity calculations, LCS-based attribute similarity calculations, and edit distance-based similarity calculations. * Token-based similarity calculation. This method uses a certain function to convert the text string to be matched into a collection of substrings, called collections of tokens, and then uses the similarity function to calculate the similarity of the two token sets to be matched, such as the Jaccard similarity function, cosine similarity function, and the qgram similarity function, etc. Different similarity functions have different characteristics. * The LCS-based similarity calculation. This method finds the longest common subsequence in two text sequences, and the length ratio of the original sequence is used as the similarity. * Edit distance-based similarity calculation. This method regards the text string to be matched as a whole, and the minimum cost of the editing operation required to convert one string to another string is used as a measure of the similarity of two strings. Basic editing operations include inserting, deleting, replacing, and swapping positions. The middle-grained model of the CFEA model we propose uses similarity calculation based on edit distance. The similarity calculation based on edit distance can effectively deal with sensitive issues such as input errors, which is of great significance to the network encyclopedia data that belongs to the user's original content UGC. Commonly used similarity functions based on edit distance are based on Levenshtein distance [34], Smith-Waterman distance [35], affine gap distance [36], or Jaro and Jaro-Winkler distance [37; 38].