RHGN : Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (2023)

Abstract: Entity Alignment, which aims to identify equivalent entities from various Knowledge Graphs (KGs), is a fundamental and crucial task in knowledge graph fusion. Existing methods typically use triple or neighbor information to represent entities, and then align those entities using similarity matching. Most of them, however, fail to account for the heterogeneity among KGs and the distinction between KG entities and relations. To better solve these problems, we propose a Relation-gated Heterogeneous Graph Network (RHGN) for entity alignment. Specifically, RHGN contains a relation-gated convolutional layer to distinguish relations and entities in the KG. In addition, RHGN adopts a cross-graph embedding exchange module and a soft relation alignment module to address the neighbor heterogeneity and relation heterogeneity between different KGs, respectively. Extensive experiments on four benchmark datasets demonstrate that RHGN is superior to existing state-of-the-art entity alignment methods.

## introduction knowledge graphs (kgs), which are sets of triples like (head entity, relation, tail entity), have been widely constructed @xcite and applied @xcite @xcite in various fields in recent years, such as dbpedia @xcite and yago @xcite . in the real world, a single kg is usually incomplete as limited sources can be collected by one kg. from this perspective, entity alignment, which aims to determine equivalent entities from various kgs, is a crucial task of knowledge graph fusion and is being increasingly researched @xcite . specifically, entity alignment is a task to find equivalent entities with the same color across two kgs, as illustrated in figure 1 . as the neighbors italy renaissance italy [appear in] (b)kg2 (a)kg1 [citizenship] leonardo da vinci [nationality] [famous people] da vinci florence florence [educate in] first, relations should not be directly incorporated into entity representation, since confusing relations with entities leads to smooth entity representations. in dbpedia, there are 4,233,000 enti-ties but only 3,000 relations, making the same relation often established between various entities (e.g., country in figure 1(b) ). to separate relations from entities, r-gcn @xcite learns relation matrices but numerous relations bring trouble for parameter optimization @xcite . therefore, existing models @xcite employ vectors to represent relations and apply simple functions (e.g., subtraction and projection) as the neighbor message functions. however, these simple functions barely distinguish relations from entities and still bring much noise to entity representation. second, due to kg heterogeneity, it is challenging to unify the semantic representations between kgs during the alignment process. specifically, kg heterogeneity includes (1) neighbor heterogeneity and (2) relation heterogeneity. neighbor heterogeneity indicates that the same entity in different kgs have different neighbors. as illustrated in figure 1 , neighbor heterogeneity is reflected in that da vinci have different neighbors in two kgs, which may make us mistakenly match da vinci in kg1 with florence cathedral in kg2 as they have more identical neighbors. relation heterogeneity means that the relation between the same entity pair can be expressed in various ways, even though these relations have similar intentions. as figure 1 shows, relation heterogeneity is expressed as that the relation between da vinci and italy is nationality in kg1, while it is citizenship in kg2, which causes trouble for aligning these triples though they have the similar meaning. to tackle these obstacles, we propose a relationgated heterogeneous graph network (rhgn) for entity alignment. specifically, we first propose a novel relation gated convolution (rgc) to make entity representations more discriminative. rgc uses relations as signals to control the flow of neighbor information, which separates relations from entities and avoids noise flowing into entities in representation learning. second, to tackle the neighbor heterogeneity between two kgs, we devise crossgraph embedding exchange (cee) to propagate information via aligned entities across different kgs, thereby unifying the entity semantics between two kgs. third, we design soft relation alignment (sra) to deal with the relation heterogeneity. sra leverages entity embedding to generate soft labels for relation alignment between kgs, hence reducing the semantic distance of similar relations across kgs. finally, extensive experiments on four realworld datasets demonstrate the effectiveness of our proposed method. the source code is available at https://github.com/laquabe/rghn . ## preliminaries in this section, we formalize the problem of entity alignment and give some related definitions. ## rhgn: relation-gated heterogeneous graph network in this section, we first present an overview of our rhgn. then we introduce the technical details of rhgn. ## limitations although we have demonstrated the superiority of our rhgh model compared to previous work on four real-world datasets, there are still two limitations that should be addressed in the future: ## conclusion in this paper, we studied the problem of entity alignment and proposed the rhgn model, which could distinguish relation and entity semantic spaces, and further address heterogeneity across different kgs. specifically, we first designed a novel relationgated convolutional layer to regulate the flow of neighbor information through relations. then, we proposed an innovative cross-graph embedding exchange module, which reduces the entity semantic distance between graphs to address neighbor heterogeneity. finally, we devised a soft relation alignment module for the unsupervised relation alignment task, which solves the relation heterogeneity problem between graphs. extensive experiments on four real-world datasets verified the effectiveness of our proposed methods. in future work, we will explore more ways to utilize the relation information in entity alignment, such as the relation path matching in different kgs.