Global entity alignment with Gated Latent Space Neighborhood Aggregation (2021)

Abstract: Existing entity alignment models mainly use the topology structure of the original knowledge graph and have achieved promising performance. However they are still challenged by the heterogeneous topological neighborhood structures which could cause the models to produce different representations of counterpart entities. In the paper we propose a global entity alignment model with gated latent space neighborhood aggregation (LatsEA) to address this challenge. Latent space neighborhood is formed by calculating the similarity between the entity embeddings it can introduce long-range neighbors to expand the topological neighborhood and reconcile the heterogeneous neighborhood structures. Meanwhile it uses vanilla GCN to aggregate the topological neighborhood and latent space neighborhood respectively. Then it uses an average gating mechanism to aggregate topological neighborhood information and latent space neighborhood information of the central entity. In order to further consider the interdependence between entity alignment decisions we propose a global entity alignment strategy i.e. formulate entity alignment as the maximum bipartite matching problem which is effectively solved by Hungarian algorithm. Our experiments with ablation studies on three real-world entity alignment datasets prove the effectiveness of the proposed model. Latent space neighborhood informationand global entity alignment decisions both contributes to the entity alignment performance improvement.

## introduction knowledge graph (kg) is an important tool to store knowledge, which provides support for many applications, such as question answering systems, recommender systems. many kgs have been constructed for particular applications. sometimes single kg cannot meet the needs of certain applications, so it is necessary to integrate multiple complementary kgs. entity alignment is non-trivial to integrating different kgs. traditional entity alignment methods are mainly divided into two categories, one is based on the entity's label information @xcite , the other is based on manually defining features. recently, the embedding-based representation learning has attracted more and more attention. given a kg, embedding-based representation learning can map entities to a lowdimensional vector space, then entity alignment model can find counterpart entities according to the similarity of the embeddings. the embedding-based entity alignment approaches show its superiority. such methods are all built based on kg embedding models (such as transe @xcite ). then they use a transformation matrix to convert the embedding space and complete the entity alignment by calculating the similarity between entity embeddings, such as mtranse @xcite . in recent researches, graph convolutional neural networks (gcn) have shown amazing results when processing graph structure data. gcn-based entity alignment models all hope that counterpart entities have similar neighborhood structures and have similar vector representations. several recent gcn-based alignment models (such as gcn-align @xcite , avr-gcn @xcite , gmnn @xcite , alinet @xcite , rdgcn @xcite ) have achieved good results. however, existing embedding-based entity alignment models still face a critical problem. the counterpart entities usually have dissimilar neighborhood structures in between kgs, which will produce different embedding. the statistics on dbpedia datasets the percentages of such entity pairs reach 89.97% between chinese-english, 86.19% between japanese-english and 90.71% between french-english, respectively @xcite . the difficulty in tackling this problem is how to alleviate the difference in topological neighborhood structure. therefore, we propose a global entity alignment model with gated latent space neighborhood aggregation (latsea). latent space neighborhood is formed in the embedding space by calculating the similarity between the entity embeddings, which can introduce long-distance neighbors to expand the topological neighborhood. the basic idea is to first learn kgs embeddings through kg embedding models and calculate the similarity between entity embeddings to generate latent space neighborhood. so, the model can be combined with many existing kg embedding models. then, we use the gcn to aggregate topological neighborhood and latent space neighborhood respectively. finally, we use an average gating mechanism to aggregate topological neighborhood information and latent space neighborhood information. state-of-the-art models treat entities autonomously when determining entity alignment results. intuitively, if a target entity is aligned to a source entity with higher confidence, it has a smaller chance to be aligned with another source entity. so, we formulate entity alignment as the maximum bipartite matching problem, which can be solved by hungarian algorithm, so that the interdependence between entity alignment decisions can be captured to make global entity alignment decisions. our experiments with ablation studies prove the effectiveness of the proposed model (latsea) and the global entity alignment strategy. in summary, our main contributions as follows: ## related work in our proposed entity alignment model, the most relevant works are kg embedding and embeddingbased entity alignment, so we introduce some of the work related. ## problem formulation formally, we represent a kg as a directed graph, where e represents the set of entities, r represents the set of relations, and t is the set of triples. each triplet (h, r, t) ∈ t indicates that the head entity h is connected to the tail entity t through the relation r. entity alignment task is to automatically find more entity pairs that denote the same real-world identity in heterogeneous kgs. entity alignment model is to take different kgs @xmath0, r, t ) and @xmath1, r , t ) as input, and find a set of identical entities s @xmath2, e ) ∈ e × e | e ≡ e } representing the same object in the real-world. those entity alignment seeds can be used as training data. ## the proposed model the goal of entity alignment is to discover the entity pairs representing the same object in heterogeneous kgs. in the paper, latsea uses hake @xcite model to learn the latent space of the kg, c c l 2 0 2 1 then it aggregates the topological neighborhood and the latent space neighborhood of the central entity through vanilla gcn respectively. the average gating mechanism controls the topological neighborhood information and latent space neighborhood information aggregation to yield central entity embeddings. then the entity similarity matrix is constructed by calculating the similarity between entity embeddings. to further consider the interdependence between entities and make global entity alignment decisions, we convert the entity similarity matrix into a bipartite graph, i.e., each row represents an entity in the source kg, and each column represents an entity in the target kg. so, the entity alignment problem is equivalent to maximum bipartite matching problem, which can be solved by hungarian algorithm. the model architecture is illustrated in figure .1. ## conclusion in this paper, we propose latsea for entity alignment, aiming at mitigating the heterogeneous among the topological neighborhood structures of counterpart entities. latsea introduces neighborhood structure in the latent space to capture distant neighbors' information, which can expand the topological neighborhood. it uses vanilla gcn to aggregate the information of adjacent entities in the topological neighborhood and latent space neighborhood, respectively. then, it uses an average gating mechanism to aggregate topological neighborhood and latent space neighborhood information. in order to further consider the interdependence between entities and make global entity alignment decisions, we propose a global entity alignment strategy, i.e., formulate entity alignment as the maximum bipartite matching problem which is effectively solved by hungarian algorithm. we evaluate latsea and global entity alignment strategy on three real-world entity alignment datasets, this result shows their effectiveness. because the contribution of distant neighbors to the central entity embeddings is different, in future work, we will introduce an attention mechanism to give different weights to neighbor entities with different contributions when aggregating latent space neighborhood information.