.png){kind=icon}
返回
Contrastive Predictive Embedding for learning and inference in knowledge graph
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2024.112730.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Knowledge graph embedding (KGE) aims to capture rich semantic information about entities and relationships in KGs, which is essential for Knowledge Graph Completion (KGC) and various downstream tasks. Existing KGE models differentiate between entity and relationship embeddings by constructing indirect pretext tasks and scoring functions to discern different types of triplets. In contrast, this paper introduces a novel KGE method called Contrastive Predictive Embedding (CPE), which dispenses with the need for defining scoring functions or negative sampling. Specifically, CPE directly predicts embeddings for unknown entities based on the known entity and relationship embeddings in triplets and compares them with the true embeddings. Additionally, this paper proposes a special optimization approach to enhance the performance of various Translation-based models. Experimental results on four benchmark KGs demonstrate that CPE improves the performance of original KGE models while maintaining lower computational complexity. On the FB15k-237 dataset, CPE enhances the MRR and Hit@k(k is an element of {1,3, 10}) metrics of TransE by 1.55%, 3.37%, 4.58%, and 5.92%, respectively.
Keyword:
Knowledge graph embedding
Contrastive learning
Knowledge graph completion
Self-supervised learning
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
机构
暂无机构信息
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
分享收藏分享收藏Impact of Age at Administration, Lysosomal Storage, and Transgene Regulatory Elements on AAV2/8-Mediated Rat Liver Transduction.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏
分享收藏
查看更多内容