# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 1 Introduction  
A knowledge graph (KG) is a structured semantic network knowledge base that stores knowledge in the form of triples, and has powerful semantic expression capabilities in the fields of natural language processing, intelligent question answering, and intelligent recommendation [1]. Driven by the rapid development of the Internet, a wealth of domain knowledge graphs have been established, e.g., recipe knowledge graphs. However, because most of the existing knowledge graphs have been established by independent institutions with different design concepts and requirements, there are differences in the described methods and descriptive focuses for the same object, resulting in the diversity and heterogeneity of data in the same domain knowledge graph [2]. However, different knowledge graphs also contain much complementary information. For instance, there are two recipe KGs, recipe 1 and recipe 2; both recipes include the dish named \"braised pork\", which is described with regard to ingredients, cuisine, and cooking techniques. However, recipe 1 emphasizes the selection and preparation of ingredients, while recipe 2 emphasizes the cooking technique. When fusing the two recipes into a unified KG, it isimperative to streamline and reorganize redundant information to optimize the structure of the KG. Additionally, the incorporation of complementary information can enhance the description of the \"braised pork\" and improve the overall quality and accuracy of the knowledge representation. Therefore, it is of great practical significance to study how to align the entities of heterogeneous knowledge graphs, in order to eliminate redundancy and integrate the knowledge graphs. As an important research topic within natural language processing, entity alignment aims to discover the heterogeneous representations of the same real-world object among heterogeneous knowledge graphs, and to establish the equivalent-link relationships among them, so as to connect multi-source knowledge graphs to form a larger and richer knowledge base [34]. It is also known as entity resolution or entity matching [5; 6]. In the process of integrating recipe KGs, we have to handle the following problems:  
* Existing methods have to compute the embeddings of all the entities and relationships, but it is unnecessary to compute some entities which represent different real-world objects. Excluding these noisy entities can reduce the number of calculations and bring significant improvement to the accuracy of entity alignment. * Existing works focus on embedding structural information; however, if there are few relationship connections between entities, this results in the sparse state of the entity neighborhood structure in domain KGs, especially for recipes. * The traditional methods generally aggregate and propagate the attribute names of entities, but ignore attribute values. However, attribute value plays a significant role in enhancing entity alignment, and each attribute value has a different influence on an entity. It is non-trivial to utilize attribute values in order to enhance entity embedding. However, existing methods cannot handle these problems, because these methods focus on enriching cross-lingual links [7; 8; 9; 10; 11], and assume that the descriptions of entities in knowledge graphs are consistent within the same language. In response to this, a novel entity alignment approach based on domain-oriented embedded representation (DomainEA) is proposed. DomainEA works as follows. In order to address the first problem, a filtering mechanism based on entity attributes first excludes the noisy entities that are impossible to align, and then selects the candidate set. For the second and third problems, a Structural Aggregator (SA) composed of Graph Convolutional Networks (GCNs) [12] employs attribute values to generate high-order neighborhood-aware embeddings for the entity structures.