# Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs
## 2 Related Work  
We discuss two lines of works that are relevant to this paper.  
**Knowledge Graph Representation Learning.** In recent years, knowledge graph representation learning has attracted the attention of researchers. The current representation learning models can be roughly divided into three categories, which are translation-based models, semantic matching models and neural-network-based models. The typical methods of translation-based models are TransE [15] and its improved models TransH [16] and TransR [17]. The core idea of TransE is to regard the relationship as the translation from the head entity to the tail entity. For any relation triples in the knowledge graph, TransE expects that the head entity vector plus the relation vector equals the tail entity vector. The semantic matching models use similarity functions to infer relational facts, e.g., the Hadamard product in ComplEx [18] and the circular correlation in HoIE [19]. The neural-network-based models exploit deep learning techniques for knowledge graph embedding. For example, ConvE [20] is a multi-layer convolution neural network which learns the representation through deep network structure and convolution operations; R-GCN [21] is a relational graph convolution neural network which generates the representation by convolving semantic information on the local graph structure. All the above models focus on relational facts and are mostly evaluated by the task of link prediction in a single knowledge graph.  
**Entity Matching.** With the emergence of various knowledge graphs in different domains, entity matching, especially cross-lingual entity matching, is becoming more and more important to solve the problem of heterogeneity. Entity matching decides whether two entities of different knowledge graphs refer to the same object. Ref. [22] transforms the entity matching problem based on attribute similarity scores into a multi-classification problem, which is divided into matching, possible matching and mismatching, and establishes the probability model of the entity matching problem. Although the above method is simple and intuitive, it requires sufficient labeled matching entity pairs and the corresponding annotation costs. Additionally, the calculation of feature similarities is often interfered with by semantic heterogeneity among different knowledge graphs. Recently, the use of the embedding models to match the entities across cross-lingual knowledge graphs has attracted extensive attention. JE [23] uses the model TransE to embed two independent knowledge graphs into the same vector space; MTransE [7] jointly trains a translation embedding model to encode language-specific knowledge graphs in separate embedding spaces and align counterpart entities across embeddings. Some studies also consider other aspects of the entity for the matching task. Some of them (e.g., MultiKE [24] and DAEA [25]) consider the name and structure of entities; others (e.g., JAPE [10], GCN-Align [26], AttrE [27] and RAKA [28]) consider entity attributes; some works (e.g., KDCoE [11] and HMAN [12]) consider the text descriptions of entities. Moreover, EVA [13] incorporates images along with structures, relations and attributes to align entities across different knowledge graphs.