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Mconvkgc: a novel multi-channel convolutional model for knowledge graph completion

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Abstract

The incompleteness of the knowledge graph limits its applications to various downstream tasks. To this end, numerous influential knowledge graph embedding models have been presented and have made great achievements in the domain of knowledge graph completion. However, most of these models only pay attention to the extraction of latent knowledge or translational features, and cannot comprehensively capture the surface semantics, latent interactions, and translational characteristics of triples. In this paper, a novel multi-channel convolutional model, MConvKGC, is presented for knowledge graph completion, which has three feature extraction channels and employs them to simultaneously extract shallow semantics, latent interactions, and translational characteristics, respectively. In addition, MConvKGC adopts an asymmetric convolutional block to comprehensively extract the latent interactions from triples, and process the generated feature maps with various attention mechanisms to further learn local dependencies between entities and relations. The results of the conducted link prediction experiments on FB15k-237, WN18RR, and UMLS indicate that our proposed MConvKGC shows excellent performance and outperforms previous state-of-the-art KGE models in the majority of cases.

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Acknowledgements

This study was supported by the Open Fund Project from Marine Ecological Restoration and Smart Ocean Engineering Research Center of Hebei Province (Grant No. HBMESO2315), the Science and Technology Project of the Hebei Education Department (Grant No. ZD2021088), and Vital Signs Detection based on milimeter-wave radar (Grant No. ZD-YG-202317-23).

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  1. Q. Chen, M. Hao, Y. Li, B. Sun: These authors contributed equally to this work.

Authors and Affiliations

  1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan, 063210, Hebei, China

    Xiaochuan Sun, Qi Chen, Mingxiang Hao, Yingqi Li & Bo Sun

  2. Key Laboratory of Industrial Intelligent Perception, Tangshan, 063210, Hebei, China

    Xiaochuan Sun, Qi Chen, Mingxiang Hao, Yingqi Li & Bo Sun

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  1. Xiaochuan Sun
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Correspondence to Yingqi Li or Bo Sun.

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Sun, X., Chen, Q., Hao, M. et al. Mconvkgc: a novel multi-channel convolutional model for knowledge graph completion. Computing 106, 915–937 (2024). https://doi.org/10.1007/s00607-023-01247-w

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