.png){kind=icon}
返回
.png){kind=icon}
.png){kind=icon}
摘要
En 中文
Knowledge graphs are organized as triplets to represent facts from the real world and play an important role in various intelligent information systems. Because knowledge graphs are frequently constructed using manual or semi-automatic methods, they often miss connections between entities. Link prediction was created to solve this problem. Many recent state-of-the-art studies, such as those introducing the RotatE and RotatHS models, have advocated for rotation transformations with entity and relation embeddings in complex vector spaces. However, using only rotation planes means that these models do not have the expressive power of models based on neural networks, such as the ConvE and the ConvR models. As a result, link prediction performance suffers. To address these shortcomings, this paper proposes the ConvRot model, which integrates a 2D convolution. Specifically, we perform convolution on embeddings of entities and relations to obtain support vector embeddings. These vectors are then integrated into an element-wise rotation from the head entity to the tail entity using the Hadamard product, enabling the model to capture local interactions among entities and relations through the neural network while still ensuring intuitiveness through a roto-transformation in the link prediction. In addition, we present two strategies for designing the complex convolution module and show their effects on model perfor-mance. The proposed method is evaluated on standard benchmark datasets and achieves significantly improved results on MRR and Hits@K (K = 1, 3, 10). Overall, our model's link prediction performance is superior by approximately 5-7 %. Moreover, the ConvRot model is also considered separately on many relation types, such as one-to-one, one-to-many, many-to-one, and many-to-many. Finally, we prove that type constraints can help increase the model's overall performance, especially on complex and large datasets.
Keyword:
Knowledge graph embedding
Link prediction
Complex convolution
Rotation transformation
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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。.png)
PREAI
.png){kind=icon}
分享.png){kind=icon}
收藏A Ranking-Based Meta-Analysis Reveals Let-7 Family as a Meta-Signature for Grade Classification in Breast Cancer.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Differences in testosterone and its precursors by sex of the offspring in meconium胎粪后代性别对睾丸激素及其前体的差异
OAAI
OAAI分享收藏
查看更多内容