A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (2022)

Abstract: Entity alignment (EA) aims to find entities in different knowledge graphs (KGs) that refer to the same object in the real world. Recent studies incorporate temporal information to augment the representations of KGs. The existing methods for EA between temporal KGs (TKGs) utilize a time-aware attention mechanisms to incorporate relational and temporal information into entity embeddings. The approaches outperform the previous methods by using temporal information. However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations. Therefore, we propose a simple GNN model combined with a temporal information matching mechanism, which achieves better performance with less time and fewer parameters. Furthermore, since alignment seeds are difficult to label in real-world applications, we also propose a method to generate unsupervised alignment seeds via the temporal information of TKG. Extensive experiments on public datasets indicate that our supervised method significantly outperforms the previous methods and the unsupervised one has competitive performance.

## introduction knowledge graphs (kgs) describe facts of the real world in a structured form of triples (h, r, t), where h represents the head entity, r represents the relation, t represents the tail entity. kgs have drawn great research attention from the academia @xcite and have been widely used to enhance downstream applications such as question answering @xcite and recommendation systems @xcite . recently, wikidata @xcite and yogo2 @xcite add temporal information to the relations to represent the * *corresponding author figure 1 : one sample of ea between tkgs. tkg1 is a sub-graph in yago. tkg2 is a sub-graph in wiki, where the entity names corresponding to the items are as follows: q39444 (ronaldinho), q7156 (fc barcelona), q166177 (ballon d'or), q1543 (a.c.milan), q59207 (ronaldo), p54 (member of sports team), p166(award received). kgs more accurately. in temporal knowledge graphs (tkgs), triples are extended to quadruples as (h, r, t, τ ), where τ represents the timestamps. figure 1 is an example of entity alignment (ea) between tkgs, where nodes represent entities, edges include relational and temporal information, and the quadruple @xcite represents one of the facts. ea seeks to find the same entities in the real world from different kgs to promote knowledge fusion. many embedding-based methods perform effective ea @xcite , which encode entities in a continuous embedding space and align entities based on the learned embeddings. previous ea methods ignore the temporal information despite their successes, a key factor indicating when a fact occurred. in order to utilize the time information in tkgs, tea-gnn @xcite first proposes a timeaware gnn for ea between tkgs, which incorporates the temporal information via a time-aware attention mechanism to learn the embeddings of entities. trea @xcite develops a temporal relational attention mechanism that introduces temporal embedding and relational embedding to entity embedding and achieves state-of-the-art (sota) performance. in different tkgs, equivalent entity and relation pairs are usually disrupted and have different representation forms. as shown in figure 1 , the entity of "ronaldinho" is stored in the form @xmath0. but in tkg2, the same entity is stored as a pure @xmath1. the relation of "plays for" is stored in the form @xmath2. therefore, we need to map different representations into a unified vector space to infer whether they are similar. however, the temporal information is uniform across most tkgs. for example, the temporal information "2005" is the same in the two tkgs, and refers to the same year. so we believe it's unnecessary to learn additional temporal embeddings. in addition, different entities can be distinguished by their temporal information even though they have similar structures and relations. such @xmath3. in summary, we can directly use the temporal information for enhancing the prediction, rather than learning the temporal embeddings, which is redundant and time-consuming. based on this finding, we propose a simple gnn model combined with a temporal information matching mechanism for entity alignment (stea) between tkgs. unlike the previous work, the model adopts a temporal information matching mechanism instead of learning temporal embeddings, which is effective and efficient. our proposed model first takes a simple gnn to learn the embeddings of entities by using their structural and relational information, then compares the entities' temporal information, and finally aligns the entities by combining their embedding similarity and time similarity. experimental results on four datasets show that our model significantly outperforms trea @xcite . the improvement scores of hits@1 are 3.4%, 3.6%, 2.2%, 12.3%, respectively. most existing methods of ea need alignment seeds to train the model and learn the embeddings of the entities. finding the alignment seeds of different kgs in the real world is labor-intensive. some unsupervised ea methods @xcite @xcite automatically generate alignment seeds via the image or name of entities. inspired by these methods, we believe that uniform temporal information can also be used to generate alignment seeds. in this paper, we as-sume that entities with the same temporal information have high similarity and calculate the time similarity of entities according to their temporal information. if two entities are both the unique nearest neighbor to each other, then the two entities are regarded as alignment seeds. experimental results show that the proposed unsupervised method outperforms the previous sota models on two datasets and competes with our supervised method stea. the main contributions of this paper are summarized as follows: ## problem formulation tkgs store the real-world knowledge in the form of quadruples (h, r, t, τ ). a tkg is represented as @xmath4, r, t, q), where e, r and t represent the sets of entities, relations and timestamps respectively, ## the proposed approach this research proposes a simple gnn model combined with a temporal information matching mechanism for ea between tkgs using the entity's structural information, relational information, and temporal information. during the training phase, the simple gnn is used to learn entities' structural and relational embeddings. in the prediction stage, the model predicts the new alignment entities by combining the embedding similarity and temporal matching degree of entities. experimental results show that our model achieves good performance by balancing the embedding similarity with the time similarity of entities. figure 2 shows the framework of stea. ## conclusion this paper proposes a simple gnn model combined with a temporal information matching mechanism for ea between tkgs, which achieves significant performance with less time and fewer parameters. by assuming that entities with the same temporal information have high similarity, we propose a method to generate the alignment seeds by temporal information. the unsupervised method outperforms the previous models on two datasets and is competitive with our supervised method. the paper does not discuss the cases where the kgs might employ different schemas for representing time. we will further explore this in future work.