基于联合语义表示的不同知识库中的实体对齐方法研究

Entity Alignment Method for Different Knowledge Repositories with Joint Semantic Representation

【目的】解决不同知识库中的实体对齐问题,探索如何有效地同时利用知识库结构信息和语义信息的实体对齐方法。【方法】利用TransE模型表示实体的结构信息,利用BERT模型表示实体的语义信息,并设计基于知识库实体的结构信息和语义信息的联合语义表示模型(BTJE),通过孪生网络实现实体对齐。【结果】本文方法在DBP-WD和DBP-YG数据集上最优MRR值分别达到0.521和0.413,Hits@1达到0.542和0.478,优于其他传统方法。【局限】实验数据集规模有限,在更大规模知识库上的通用性有待考证。【结论】探索了一种基于联合语义表示的不同知识库中的实体对齐方法,通过在模型中同时引入实体的结构信息和语义信息,有效提高了模型对实体的表示能力,从而在不同知识库中的实体对齐任务中有较好的性能。

[Objective]This paper combines the structure and semantic information of knowledge,aiming to create a better entity alignment method for different knowledge repositories.[Methods]First,we used the TransE model to represent the structure of entities,and used the BERT model to represent their semantic information.Then,we designed an entity alignment method based on the BTJE model(BERT and TransE Joint model for Entity alignment).Finally,we use the siamese network model to finish entity alignment tasks.[Results]We examined the new method with DBP-WD and DBP-YG datasets.Their optimal MRR values reached 0.521 and 0.413,while the Hits@1 reached 0.542 and 0.478.These results were better than those of the traditional models.[Limitations]The size of our experimental data set needs to be expanded,which will further evaluate the performance of the proposed method.[Conclusions]Our new method could effectively finish entity alignment tasks for different knowledge bases.