# Coarse-to-Fine Entity Alignment for Chinese Heterogeneous Encyclopedia Knowledge Base
## 3 CFEA Model
### Overview of CFEA  
Figure 3 shows the framework of the CFEA model we designed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(e\) & entity \(e\) \\ \(KB\) & knowledge base \\ \(E\) & set of knowledge base dataset \\ \(AE\) & aligned set of knowledge base dataset \\ \(a\) & weight of URL in entity’s attributes \\ \(c\) & cutoff threshold of matched attributes \\ \(u\) & upper threshold in attributes match \\ \(v\) & lower threshold in attributes match \\ \(\omega\) & threshold in LDA model \\ \(K\) & number of topics \\ \(N\) & number of words \\ \(D\) & number of documents \\ \(\alpha\) & a priori parameter of the LDA model \\ \(\beta\) & a priori parameter of the LDA model \\ \(z_{n}\) & a topic of word \(n\) \\ \(w_{n}\) & a word with index \(n\) \\ \(\theta_{d}\) & documents-topics matrix for document \(d\) \\ \(\phi_{k}\) & topics-words matrix for topic \(k\) \\ \(\textit{LevD}(\textit{str}_{a},\textit{str}_{b})\) & Levenshtein distance of string a and string b \\ \(W_{e}\) & set of feature words of entity \(e\) \\ \(feature_{e}\) & feature probability matrix of entity \(e\) \\ \(V_{e}\) & feature vector of entity \(e\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation Overview.  
Figure 2: Illustration of knowledge base entity alignment (reproduced from [46]). \(KB_{1}\) and \(KB_{2}\) are two different knowledge bases, and Entity1 and Entity2 are the entities in the two knowledge bases, respectively. Both entities refer to the same entity Entity0 in the real world.  
The steps of the CFEA model as applied to a heterogeneous encyclopedia knowledge base are as follows. (1) We first extract the attributes, abstracts, and descriptions of the entities in the dataset, and combine the latter two as contextual information. (2) Secondly, we block the knowledge base and build the topic feature base. The blocking algorithm refers to grouping entities that meet the attribute requirements into the corresponding categories. It is the coarse-grained part of the CFEA. The construction of the topic feature base requires all contexts to be processed by Chinese word separation and input to the LDA model for unsupervised training, which, in turn, yields the latent topic features of the entity. (3) After that, it is the process of aligning the entities. For pairs of entities to be aligned from different data sources, we use the method of the edit distance-based similarity calculation of attributes as the middle-grained part for preliminary judgment. If the similarity score meets the upper boundary of the threshold, the pair of entities is directly judged as aligned. If beneath the lower threshold, pairs are determined as non-aligned. Otherwise, it is between the upper and lower threshold, which means that the current attribute information is insufficient for judgment. (4) At last, we explore the fine-grained perspective, with the well-trained LDA topic model. We further consider the contextual information of the current entities and perform the calculation of topic similarity. After the above steps, all matching entities are generated in a coarse-to-fine process.  
The process of coarse-to-fine entity alignment, described in Algorithm 1.  
Human cognition is a process of refinement from coarse-grained perspectives to a fine-grained perspectives [47]. Most existing collective entity alignment methods have high time complexity. Therefore, we introduce the human brain's \"global precedence\" cognitive mechanism. We first obtain the category to which each entity belongs as the coarse-grained step and weed out candidates with different types in the following process of entity alignment. As these parts of the entities cannot reach a match with other types of entities, in this way, our approach achieves a reduction in runtime by reducing the size of the candidate knowledge base.