.png){kind=icon}
返回
MMIEA: Multi-modal Interaction Entity Alignment model for knowledge graphs
.png){kind=icon}
PRE
AI
DOI：10.1016/j.inffus.2023.101935.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Fusing data from different sources to improve decision making in smart cities has received increasing attention. Collected data through sensors usually exist in a multi-modal form, such as values, images, and texts. Thus, designing models that handle multi-modal data has an important role in this field. Meanwhile, security and privacy issues cannot be ignored, as the leakage of big data may provide opportunities for criminals. To solve the above challenges, we focus on research on multi-modal entity alignment for knowledge graphs and proposed the Multi-Modal Interaction Entity Alignment model (MMIEA). The model is proposed from the perspective of fusing data from different modalities while maintaining privacy. We determined that the model is privacy-preserving because it does not need to transmit the raw data of each modality (only the vector representation is transmitted). Specifically, we introduce and improve the BERT-INT model for the entity alignment task in multi-modal knowledge graphs. Experimental results on two commonly used multi -modal datasets show that our method outperforms 17 algorithms, including nine multi-modal entity alignment methods.
Keyword:
Multi-modal
Knowledge graph
Entity alignment
Interaction
BERT
期刊
IF:
15.5
论文数:
4.3K
被引数:
2.7W
机构
引用论文
Kinematic analysis of limb movements in neuropsychological research: Subtle deficits and recovery of function.神经心理学研究中肢体运动的运动学分析: 细微的缺陷和功能的恢复。
PREAI
分享收藏DBpedia - A large-scale, multilingual knowledge base extracted from WikipediaDBpedia-从维基百科中提取的大规模多语言知识库.png)
SEMANTIC WEB
OAAI
SEMANTIC WEBIF2.9
OAAI分享收藏
查看更多内容