Learning knowledge graph embedding with multi-granularity relational augmentation network

doi:10.1016/j.eswa.2023.120953

Learning knowledge graph embedding with multi-granularity relational augmentation network

2023-12-01

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DOI：10.1016/j.eswa.2023.120953

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Knowledge graph embedding (KGE) aims to complete link prediction tasks effectively by learning the representation of entity and relation. Recently, deep neural networks have achieved prominent results for learning KGE. However, knowledge graphs typically contain massive information about entities and relations, and existing deep neural network-based KGE models exploit semantic information from simple explicit feature concatenation and reshaping without considering bidirectional implicit interactions, which cannot accentuate relevant predictive information. Additionally, these models simply extract nonlinear features from embedded matrices of relations, resulting in neglecting the distinctive semantic connotation of heterogeneous relations. To overcome these two challenges, a multi-granularity relational augmentation network (MRAN) to learn KGE is proposed. Specifically, multi-granularity implicit interaction embeddings are generated to facilitate relevant predictive information selection, which model bidirectional implicit interactions between entities and relations in Euclidean space, complex space, and quaternion space. Moreover, relational augmentation convolution module utilizes relation-aware filters at the convolutional layer to preserve the distinctive relational properties for dealing with heterogeneous relations. Subsequently, the augmentation mechanism is injected to focus on informative features and restrain useless features of neural networks, which boosts the quality of KGE. Experimental results demonstrate the outstanding link prediction results of MRAN on five benchmark datasets, revealing the benefits of considering bidirectional implicit interactions and heterogeneous relations. Our model substantially improves efficiency while achieving excellent performance, which is superior to state-of-the-art baseline models in 2 out of 4 metrics on WN18 and in 3 out of 4 metrics on FB15k-237 and YAGO3-10 datasets.

Keywords:

Knowledge graph embedding

Multi -granularity implicit interaction

Heterogeneous relation

Augmentation feature

Learning knowledge graph embedding with multi-granularity relational augmentation network

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