# Coarse-to-Fine Entity Alignment for Chinese Heterogeneous Encyclopedia Knowledge Base
## 2 Related Work
### Collective Alignment
For example, two similarly named authors who have a \"coauthor\" relationship with two aligned author entities may have a higher degree of similarity, and then this similarity is propagated to more entities. The other way is based on a probability model. This type of method establishes a complex probability model for entities with which to match relationships and match decision-making. Generally, statistical relational learning is used for calculation and reasoning, and methods such as logical representation, probabilistic reasoning, uncertainty processing, machine learning, and data mining are integrated with relations to obtain the likelihood model in relational data. Bayesian network models [22; 23], LDA models [24; 25], CRF (conditional random field) models [26; 27] and MLN (Markov logic networks) [28; 29] models are commonly used probability models. Probabilistic models provide a standard method of relationship modeling, which can effectively improve the matching effort. Among them, the LDA model is an unsupervised model that mines the potential topic information of the text. It can simplify the data representation in a large-scale data set and retain basic information for data analysis, such as correlation, similarity, or clustering. It has the advantages of wide applicability and adaptability to large-scale data sets. Among these probabilistic models, latent Dirichlet allocation (LDA) [30] is a three-layer Bayesian probabilistic model that consists of three layers: words, topics, and documents. It utilizes priori parameters \(\alpha,\beta\), the corpus document, \(D\), and the parameters \(\theta_{d},\phi_{k}\) to automatically generate the document and obtain the the posteriori \(P(\theta,\phi|D)\) by learning the corpus document. The derived parameters \(\theta_{d},\phi_{k}\) can determine the topic of the document at the semantic level. Figure 1 shows the Bayesian network diagram of the process of document generation with the LDA model. The corresponding process of document generation is as follows. 1. Sampling in the distribution \(Dirichlet(\vec{\alpha})\) to get the topic distribution \(\theta_{d}\) of document \(d\)2. Sampling in the distribution \(Multinomial(\theta_{d})\) to get the topic \(z_{n}\) of word \(n\) in document \(d\). 3. Sampling in the distribution \(Dirichlet(\vec{\beta})\) to get the word distribution \(\phi_{k}\) of topic \(d\) (denoted as \(k\)). 4. Sampling in the distribution \(Multinomial(\phi_{k})\) to get the word \(w_{n}\) Finally, the document is generated after the above multiple sampling. The CFEA (coarse-to-fine entity alignment) model proposed in this paper combines paired entity alignment and collective entity alignment. The middle-grained model (detail in Section 3.4) only considers the attribute information of the entity pair currently to be matched, thus, it is a paired-entity alignment method. The fine-grained model (detail in Section 3.5) mainly uses the LDA method. The LDA topic model obtains additional entity information that cannot be obtained from attribute information from the semantic level of the text to help align entities. This is a global collective entity alignment method. It performs topic modeling on all texts in the data set, and there is a topic-level influence between any two entities.