Complex Knowledge Graph Embeddings Based on Convolution and Translation

Lin Shi, Zhao Yang, Zhanlin Ji () and Ivan Ganchev ()
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Lin Shi: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Zhao Yang: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Zhanlin Ji: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Ivan Ganchev: Telecommunications Research Centre (TRC), University of Limerick, V94 T9PX Limerick, Ireland

Mathematics, 2023, vol. 11, issue 12, 1-14

Abstract: Link prediction involves the use of entities and relations that already exist in a knowledge graph to reason about missing entities or relations. Different approaches have been proposed to date for performing this task. This paper proposes a combined use of the translation-based approach with the Convolutional Neural Network (CNN)-based approach, resulting in a novel model, called ConCMH. In the proposed model, first, entities and relations are embedded into the complex space, followed by a vector multiplication of entity embeddings and relational embeddings and taking the real part of the results to generate a feature matrix of their interaction. Next, a 2D convolution is used to extract features from this matrix and generate feature maps. Finally, the feature vectors are transformed into predicted entity embeddings by obtaining the inner product of the feature mapping and the entity embedding matrix. The proposed ConCMH model is compared against state-of-the-art models on the four most commonly used benchmark datasets and the obtained experimental results confirm its superiority in the majority of cases.

Keywords: knowledge graph embedding (KGE); translation-based model; CNN-based model; test leakage (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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