# Coarse-to-Fine Entity Alignment for Chinese Heterogeneous Encyclopedia Knowledge Base
## 2 Related Work
### Collective Alignment  
From the perspective of the scope of information to be considered, EA methods can be divided into pairs-wise alignment, local collective alignment, and global collective alignment [15]. The paired-entity alignment method is a method of comparing the attribute information of the current matching entities in pairs. This method considers the similarity of entity attributes or specific attribute information, but does not consider the relationships between matching entities. [PERSON] [16] and [PERSON] [17] established a probabilistic model of the entity-matching problem, based on attribute similarity scores, by converting this problem into a classification problem,which is divided into matching, possible matching, and non-matching. This model is an important method of entity alignment. An intuitive classification method is to add the similarity scores of all attributes to obtain the total similarity score, and then set two similarity thresholds to determine which similarity interval the total similarity score \(sim_{attr}^{sum}\) is within. This can be expressed as:  
\[\left\{ \begin{array}{lcl}u\leq sim_{attr}^{sum}(e_{1},e_{2})&\Rightarrow e _{1},e_{2}\text{ matched}\\ v\leq sim_{attr}^{sum}(e_{1},e_{2})<u&\Rightarrow e_{1},e_{2}\text{ possible matched}\\ sim_{attr}^{sum}(e_{1},e_{2})<v&\Rightarrow e_{1},e_{2}\text{ mismatched}\\ \end{array} \right. \tag{1}\]  
Here, \(e_{1}\), \(e_{2}\) are the entity pairs to be matched; \(u\) and \(v\) are the upper and lower thresholds; \(sim_{attr}^{sum}\) firstly calculates matched pairs of attributes from \(e_{1},e_{2}\) respectively and then sums them to get a total similarity score. The main problem with this simple method is that it does not reflect the influence of different attributes on the final similarity. Animportant solution is to assign different weights to each matched attribute to reflect its importance to the alignment result. [PERSON] described this idea formally by establishing a probability-based entity link model based on the Fellegi-Sunter model [18]. The paired entity alignment method is simple to operate and convenient to calculate, but it has the disadvantage of considering less information and lacking semantic information. The middle-grained part in the CFEA model proposed in this paper is also constructed based on the classification idea and on the distribution weight idea, which belongs to the paired-entity alignment method. Local collective entity alignment not only considers the attribute similarity of matching entity pairs, but also considers the similarity of their relationships. One method is to assign different weights to the attributes of the entity itself and the attributes of its related neighbor entities, and to calculate the overall similarity by weighted summation. This method can be formalized as:  
\[sim(e_{1},e_{2})=asin_{attr}(e_{1},e_{2})+(1-\alpha)sim_{NB}(e_{1},e_{2}) \tag{2}\]  
and  
\[ \begin{array}{l}sim_{attr}(e_{1},e_{2})=\sum_{(a_{1},a_{2})\in attr(e_{1},e_ {2})}sim(a_{1},a_{2});\\ sim_{NB}(e_{1},e_{2})=\sum_{(e_{1}^{\prime},e_{2}^{\prime})\in NB(e_{1},e_{2}) }sim_{attr}(e_{1}^{\prime},e_{2}^{\prime}).\end{array}  \tag{3}\]  
where \(sim_{attr}(e_{1},e_{2})\) is the attribute similarity function of the entity pair; \(sim_{NB}(e_{1},e_{2})\) is the degree of neighbor similarity in the entity pair function; \(0\leq\alpha\leq 1\) is the adjustment parameter of the above two functions. This method takes the relationship of the entity as a special attribute of the entity and brings it into the calculation. In essence, it is still a paired-entity alignment method. The local collective entity alignment method does not really employ a \"collective\" approach. The global collective entity alignment is a method of entity alignment that truly realizes the collective approach. One way is collective alignment based on similarity propagation. This method iteratively generates new matches [19; 20; 21] in a \"bootstrapping\" manner through initial matching.