Multi-Channel Graph Neural Network for Entity Alignment

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1452–1461 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 1452 Multi-Channel Graph Neural Network for Entity Alignment Yixin Cao1 Zhiyuan Liu2 Chengjiang Li3 Zhiyuan Liu3 Juanzi Li3 Tat-Seng Chua1 1School of Computing, National University of Singapore, Singapore 3School of Science, Xi’an Jiaotong University, Xi’an, China 3Department of CST, Tsinghua University, Beijing, China {caoyixin2011,acharkq,iamlockelightning}@gmail.com {liuzy,lijuanzi}@tsinghua.edu.cn, dcscts@nus.edu.sg Abstract Entity alignment typically suffers from the is- sues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by ro- bustly encoding two KGs via multiple chan- nels. Each channel encodes KGs via differ- ent relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive enti- ties respectively, which are further combined via pooling techniques. Moreover, we also in- fer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make better use of seed align- ments. Extensive experiments on five pub- licly available datasets demonstrate our su- perior performance (5% Hits@1 up on av- erage). Source code and data used in the experiments can be accessed at https:// github.com/thunlp/MuGNN. 1 Introduction Knowledge Graphs (KGs) store the world knowl- edge in the form of directed graphs, where nodes denote entities and edges are their relations. Since it was proposed, many KGs are constructed (e.g., YAGO (Rebele et al., 2016)) to provide struc- tural knowledge for different applications and lan- guages. These KGs usually contain complemen- tary contents, attracting researchers to integrate them into a unified KG, which shall benefit many knowledge driven tasks, such as information ex- traction (Cao et al., 2018a) and recommenda- tion (Wang et al., 2018a). It is non-trivial to align different KGs due to their distinct surface forms, which makes the sym- bolic based methods (Suchanek et al., 2011) not Northeastern Mandarin Jilin City Province Dialect 吉林林省(Jilin) Province Dialect 刘⾮非(Liu Fei) Dialect Mayor KG1 KG2 东北北话(Northeastern Mandarin） 吉林林市 (Jilin City) Changchun ⻓长春 (Changchun) Nearby Capital Capital Jilin Figure 1: Illustration of the structural differences (dashed lines and ellipse) between different KGs. always effective. Instead, recent work utilizes gen- eral KG embedding methods (e.g., TransE (Bor- des et al., 2013)) and align equivalent entities into a unified vector space based on a few seed align- ments (Chen et al., 2017; Sun et al., 2017; Zhu et al., 2017; Chen et al., 2018; Sun et al., 2018; Wang et al., 2018b). The assumption is that enti- ties and their counterparts in different KGs should have similar structures and thus similar embed- dings. However, alignment performance is unsat- isfactory mainly due to the following challenges: Heterogeneity of Structures Different KGs usu- ally differ a lot, and may mislead the representa- tion learning and the alignment information from seeds. Take the entity Jilin City as an example (Figure 1), KG1 and …

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