Multimodal knowledge graph representation learning with entity description

Yuhang Sun; Licai Wang; Yangchen Huang; Zhichao Zhang

doi:10.1117/12.2684616

21 July 2023 Multimodal knowledge graph representation learning with entity description

Yuhang Sun, Licai Wang, Yangchen Huang, Zhichao Zhang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127171K (2023) https://doi.org/10.1117/12.2684616
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

The representation learning of knowledge graph aims to represent the semantic information of entities and relations as dense low-dimensional real-valued vectors and map them to the same low-dimensional space. Existing methods often focus on the single-modal information of the text and ignore the information of the image modality, resulting in the ineffective use of entity feature information in the image. And there is entity-related descriptive information in most knowledge graphs, which are not well used in current multimodal knowledge representation learning methods. In this regard, a multimodal knowledge representation learning method combining description information is proposed. This method combines multimodal (image, text) data to construct a knowledge representation learning model and combines its corresponding brief description information to improve the representation effect of multimodal data. Experimental results show that the method performs well on triple classification and link prediction tasks on the constructed WI-D dataset.

Citation Download Citation

Yuhang Sun, Licai Wang, Yangchen Huang, and Zhichao Zhang "Multimodal knowledge graph representation learning with entity description", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127171K (21 July 2023); https://doi.org/10.1117/12.2684616

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KEYWORDS

Data modeling

Semantics

Head

Tunable filters

Windows

Image classification

Convolutional neural networks

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