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Knowledge graph incremental embedding for unseen modalities

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Abstract

Knowledge graph embedding (KGE) projects entities and relations into low-dimension continuous vector space, which promotes the widespread use of knowledge graphs in many downstream applications. Despite the fact that most of existing KGE models focus on static single-modal knowledge graphs, many knowledge graphs are incremental and multi-modal in the real world. Especially, the incremental entities are accompanied by new modalities, i.e., unseen modalities in background knowledge graphs. To well address the novel task, i.e., incrementally embed the new entities with unseen modal information effectively and efficiently, we propose a novel incremental multi-modal knowledge graph embedding model entitled Multi-Modal Rotating on Hyperplanes, which consists of the following two modules. (1) To gain a high-quality background KGE space, the module Background Knowledge Graph Embedding Module is developed to fuse seen modal information with a gated multi-modal encoder and decode the triples by a rotation-based KGE model. (2) The module Incremental Knowledge Graph Embedding Module is designed to fuse unseen modal information of incremental entities and incrementally embed the new entities into the trained embedding space. Extensive experiments are conducted on two real-world multi-modal datasets, and the results demonstrate the superiority of the proposed model in terms of both effectiveness and efficiency compared with the state-of-the-art approaches.

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The datasets WN9-IMG and FB-IMG in the experiments are public datasets.

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Acknowledgements

This work was supported by the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 19KJA610002, the National Natural Science Foundation of China under Grant Nos. 61902270 and No. 62272332, and the Ningbo Science and Technology Special Project with Grant No. 2021Z019.

Funding

The funding concludes the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 19KJA610002, the National Natural Science Foundation of China under Grant Nos. 61902270 and 62272332, and the Ningbo Science and Technology Special Project with Grant No. 2021Z019.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Ganjiangdong, Suzhou, 215006, Jiangsu, China

    Yuyang Wei, Wei Chen, An Liu & Lei Zhao

  2. School of Computer and Data Engineering, NingboTech University, Shounan, Ningbo, 315100, Zhejiang, China

    Shiting Wen

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  1. Yuyang Wei
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  2. Wei Chen
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  3. Shiting Wen
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  4. An Liu
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  5. Lei Zhao
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Contributions

YW and WC wrote the main manuscript. SW, AL, and LZ wrote the abstract and the section “Introduction.” SW, AL, and LZ prepared Fig. 1 and Table 1. All authors reviewed the manuscript.

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Correspondence to Wei Chen or Lei Zhao.

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Wei, Y., Chen, W., Wen, S. et al. Knowledge graph incremental embedding for unseen modalities. Knowl Inf Syst 65, 3611–3631 (2023). https://doi.org/10.1007/s10115-023-01868-9

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