Fusing Attribute Character Embeddings with Truncated Negative Sampling for Entity Alignment

Abstract

Finding pairs of entities from two different knowledge graphs that reflect the same real-world object is the purpose of entity alignment for knowledge graphs. In recent years, techniques that use entity alignment for knowledge fusion have received widespread attention. In this article, we suggest a method for entity alignment using truncated negative sampling with attribute character embedding. The method mainly makes use of the relationship and attribute data in heterogeneous knowledge graphs to fulfil the entity alignment task. Firstly, the framework uses relationship mapping to unify the namespace of heterogeneous relationships. Secondly, the attribute character embeddings are generated using the attribute triples in the knowledge graph to unify the embedding space of heterogeneous entities. Then, the entity similarity between heterogeneous knowledge graphs is captured by structural embedding. Next, to learn more useful semantic information during negative sampling, the framework adopts a truncated negative sampling strategy to increase the generalizability of the model. The negative sampling procedure employs targets with high similarity to the target entity as negative sample targets. Finally, we performed comparison tests on two well-known real-world datasets, and the outcomes demonstrate that the proposed model outperforms three other representative advanced approaches, especially with an over 10% improvement in the Hits@k metric compared to the baseline method.