# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 2 Related Work  
The existing entity alignment methods can be divided into three categories, namely statistics methods, classifier methods, and representation learning methods. The core idea of the statistical methods is to find equivalent entities by comparing the similarity of entity names, entity attributes, and attribute values. In order to resolve the problems of a large amount of calculations and the method being limited to the calculation of the concept in ontology mapping, an improved mapping method of comprehensive ontology similarity [14] was proposed. This divides the large size of the original ontology into some smaller ontologies, and then computes the ontology conceptual similarity by combing three weighted similarities, which are derived from ontology concepts based on the How Net, semantics, and structure. Similar methods include PARIS [15] and RiMOM-IM [16]. Some methods [17] first calculate the similarity of each attribute via a variety of similarity measures, and then select the attribute similarity with the best performance to construct an integrated statistical model. The literature [18] uses the clustering model to train an adaptive distance function through the sample dataset, and through this function, the entities that may be aligned are clustered together. However, in heterogenous knowledge bases it is difficult for the accuracy of statistical methods to reach a sufficient value, and such methods need to manually define some hyperparameters that quantify the degree of similarity between entities. It is clear that this kind of method requires the involvement of domain experts, incurring additional labor costs. The core idea of the classifier methods [19; 20] is to model the similarity of each attribute of the entity as a feature value, and then to transform the alignment problem into a two-classification or three-classification problem, and finally to complete the alignment task through the decision results obtained by the classifier model. These kinds of methods are decision trees, Support Vector Machines (SVM), ensemble learning algorithms, and so on. Some researchers [19] have proposed a decision model based on cost optimization, which produces a cost-optimal decision rule with the overall cost formula and Bayesian formula, and implements entity alignment according to the rule. The literature [20] employs machine learning techniques to analyze the semantic features of the associated dataset, and proposes an approach for aligning entities founded on the semantic features of the textual attributes related to the associated dataset. Compared to statistical methods, the classifier methods achieve a significant improvement in accuracy. However, such methods need to deliberately design different feature engineering strategies for different knowledge bases, so that the applicability of these classifier methods is limited and the portability is poor. The representation learning methods mainly utilize the Knowledge Graph Embedding (KGE) technology, which learns the low-dimensional vector representations of the entities that capture their implicit features, and aligns the different entities which represent the same real-world object via these semantic embeddings. Such methods can automatically obtain entity pairs from heterogeneous knowledge graphs without a large number of artificial features, so that they can achieve a wider range of applications [5]. Representation-based learning methods can be subdivided into two categories. One is a semantic matching model, which assumes that a relationship between two entities can be translated as a vector in the bedding space. Based on the above hypothesis, TransE [21] is a translation mode which represents entity relationships in the knowledge base as translation operations of head and tail entities on low-dimensional vectors.