Online Updates of Knowledge Graph Embedding

Author(s):  
Luo Fei ◽  
Tianxing Wu ◽  
Arijit Khan
Author(s):  
A-Yeong Kim ◽  
◽  
Hee-Guen Yoon ◽  
Seong-Bae Park ◽  
Se-Young Park ◽  
...  

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.


Author(s):  
Wei Song ◽  
Jingjin Guo ◽  
Ruiji Fu ◽  
Ting Liu ◽  
Lizhen Liu

2021 ◽  
pp. 107181
Author(s):  
Yao Chen ◽  
Jiangang Liu ◽  
Zhe Zhang ◽  
Shiping Wen ◽  
Wenjun Xiong

2021 ◽  
Author(s):  
Shensi Wang ◽  
Kun Fu ◽  
Xian Sun ◽  
Zequn Zhang ◽  
Shuchao Li ◽  
...  

2021 ◽  
Author(s):  
Haoyang Shi ◽  
Yulan Zhang ◽  
Zhanyang Xu ◽  
Xiaolong Xu ◽  
Lianyong Qi

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