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RIECN: learning relation-based interactive embedding convolutional network for knowledge graph

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Abstract

Most knowledge graphs(KGs) are large and incomplete graph-structure database, which can be completed by predicting miss links according to the existing knowledge. The mainstream method is knowledge graph embedding (KGE) which is designed to learn low dimensional embedding of entities and relations. However, knowledge graph embedding still faces two major issues: (1) How to generate more expressive embeddings? (2) How to solve semantic polysemy of entities in different relations? In this paper, we propose a novel KG embedding model, RIECN (Relation-based Interactive Embedding Convolutional Network), which achieves high-quality performance and shows some advancements in modeling complex relations. In RIECN, FIR (Feature Interaction Reshaping) method is introduced to increase the feature interactions between entity and relation embeddings to generate more expressive feature maps. In addition, a new method of generating relation-based dynamic convolution filters, RDCF, is proposed. RDCF generates specific relation and hybird-size convolution filters, which enriches the feature maps of each entity improving the accuracy of link prediction task especially in complex relations scenario. We tested the performance of our model on five benchmark datasets. The experimental results show that the RIECN model significantly outperforms recent state-of-the-art models by 0.1–3.2% and 1.1–3.7%, in terms of MMR metric and Hit@1 metric, respectively.

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Data availability

The datasets generated during and/or analysed during the current study are available in the OpenKE repository, [https://github.com/thunlp/OpenKE/tree/OpenKE-PyTorch/benchmarks].

Notes

  1. https://github.com/thunlp/OpenKE/tree/OpenKE-PyTorch/benchmarks.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under 62277028, Central Universities of CCNU under Grant CCNU19ZN013 and Key Technologies R\({ \& }\)D Program of China Xinjiang Production and Construction Corps of data-driven regional intelligent education service (2021AB023).

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  1. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China

    Wei Wang, Xiaoxuan Shen, Huanyu Zhang, Zhifei Li & Baolin Yi

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  1. Wei Wang
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  2. Xiaoxuan Shen
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Correspondence to Baolin Yi.

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Wang, W., Shen, X., Zhang, H. et al. RIECN: learning relation-based interactive embedding convolutional network for knowledge graph. Neural Comput & Applic 35, 8343–8356 (2023). https://doi.org/10.1007/s00521-022-08109-0

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