Knowledge Graph Alignment with Entity-Pair Embedding (2020)

Abstract: Knowledge Graph (KG) alignment is to match entities in different KGs, which is important to knowledge fusion and integration. Recently, a number of embedding-based approaches for KG alignment have been proposed and achieved promising results. These approaches first embed entities in low-dimensional vector spaces, and then obtain entity alignments by computations on their vector representations. Although continuous improvements have been achieved by recent work, the performances of existing approaches are still not satisfactory. In this work, we present a new approach that directly learns embeddings of entity-pairs for KG alignment. Our approach first generates a pair-wise connectivity graph (PCG) of two KGs, whose nodes are entity-pairs and edges correspond to relation-pairs; it then learns node (entity-pair) embeddings of the PCG, which are used to predict equivalent relations of entities. To get desirable embeddings, a convolutional neural network is used to generate similarity features of entity-pairs from their attributes; and a graph neural network is employed to propagate the similarity features and get the final embeddings of entity-pairs. Experiments on five real-world datasets show that our approach can achieve the state-of-the-art KG alignment results.

## introduction knowledge graphs (kgs) have been built and applied in several domains, including question answering @xcite , recommendation @xcite , and information extraction @xcite . most existing kgs are built separately by different organizations, using different data sources and languages. therefore, kgs are heterogeneous that the same entity may exist in different kgs in different surface forms. on the other hand, kgs can be complementary to each other; knowledge about the same entity may distribute in several kgs. to handle the heterogeneity problem and integrate knowledge in different kgs, it is essential to perform kg alignment, i.e. matching entities in separate kgs. recently, kg embedding models have been explored in solving the problem of kg alignment. a number of embedding-based approaches have been proposed, including mtranse @xcite , jape @xcite , iptranse @xcite , gcn-align @xcite , rdgcn @xcite , and multike @xcite , etc. these approaches first embed entities in low-dimensional vector spaces, and then obtain the entity alignments by computations on their vector representations. comparing with traditional similarity-based approaches, embedding-based ones can effectively model different kinds of information in kgs, which align entities without manually designed similarity features. most recently, continuous improvements have been achieved by combining multiple kinds of information in kgs or using more sophisticated embedding models. however, the performances of most approaches are still not satisfactory. according to the results in a recent work @xcite , a traditional unsupervised alignment approach, logmap @xcite , outperforms most existing embedding-based approaches. to get more accurate alignment results, we propose an entity-pair embedding approach for kg alignment (epea). instead of learning embeddings of single entities, our approach directly learns representations of entity-pairs. similarity features of entities' attribute information are automatically extracted, which are then propagated using structure information of entities. equivalent relations of entities can be accurately predicted based on the learned embeddings of entity-pairs. specifically, our work has the following contri-butions: the rest of this paper is organized as follows: section 2 formalizes the entity alignment problem, section 3 describes our proposed approach, section 4 presents the evaluation results, section 5 discusses some related work, and section 6 is the conclusion. ## the proposed approach figure 2 shows the framework of our approach. given two kgs, our approach first generates the pcg of them. then, a cnn-based feature extraction method is used to generate node representations from the attribute information of entities. at last, an attention-based feature propagation is performed over the pcg to incorporate structure information into the node representations. entity alignments are predicted based on the learned embeddings of entity-pairs. in the following, we present our approach in detail. ## conclusion this paper presents a new entity-pair embedding approach for kg alignment. our approach first extracts useful attribute features of entity-pairs by using a convolutional neural network, and then propagates the features among the neighbors of entitypairs, by using a graph neural network with edgeaware attentions. the embeddings are learned with the object of separating equivalent and nonequivalent entity-pairs. experiments on five real-world datasets show that our approach achieves the stateof-the-art results.