A cross-linguistic entity alignment method based on graph convolutional neural network and graph attention network

Zhao, Zhijia; Lin, Shou-De

doi:10.1007/s00607-023-01178-6

Cited by 3 publications

(1 citation statement)

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“…MHNA [32] improves the alignment performance by designing a variable attention mechanism based on heterogeneous graphs. To better utilize the entity attribute information, GRGCN [33] employs a combination of graph convolutional neural networks and graph attention networks for entity alignment. EMGCN [34] is an unsupervised KG entity alignment approach that utilizes a late fusion mechanism to integrate the rich relational triples.…”

Section: Entity Alignmentmentioning

confidence: 99%

A Novel Method for Boosting Knowledge Representation Learning in Entity Alignment through Triple Confidence

Zhang,

Chen,

Wang

2024

Mathematics

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Entity alignment is an important task in knowledge fusion, which aims to link entities that have the same real-world identity in two knowledge graphs. However, in the process of constructing a knowledge graph, some noise may inevitably be introduced, which must affect the results of the entity alignment tasks. The triple confidence calculation can quantify the correctness of the triples to reduce the impact of the noise on entity alignment. Therefore, we designed a method to calculate the confidence of the triples and applied it to the knowledge representation learning phase of entity alignment. The method calculates the triple confidence based on the pairing rates of the three angles between the entities and relations. Specifically, the method uses the pairing rates of the three angles as features, which are then fed into a feedforward neural network for training to obtain the triple confidence. Moreover, we introduced the triple confidence into the knowledge representation learning methods to improve their performance in entity alignment. For the graph neural network-based method GCN, we considered entity confidence when calculating the adjacency matrix, and for the translation-based method TransE, we proposed a strategy to dynamically adjust the margin value in the loss function based on confidence. These two methods were then applied to the entity alignment, and the experimental results demonstrate that compared with the knowledge representation learning methods without integrating confidence, the confidence-based knowledge representation learning methods achieved excellent performance in the entity alignment task.

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Section: Entity Alignmentmentioning

confidence: 99%