CASIA OpenIR  > 舆论大数据科学与技术应用联合实验室
Relation Adaptive Representation Learning Based on Factual Information Interaction for One-Shot Knowledge Graph Completion
Li JL(李金林)1,2; Wang ZK(王子康)1,2; Li LJ(李林静)1,2; Ceng DJ(曾大军)1,2
2024
Conference NameInternational Joint Conference on Neural Networks
Conference Date2024-6-30
Conference Place日本横滨
Abstract

Few-shot, especially one-shot learning is a prominent
research area in the field of knowledge graphs (KGs), aiming
to utilize a limited number of triples with unseen relations as
reference information for inferring missing knowledge. Recent
research focuses on improving the semantic representation of
entity pairs using interactions between their head and tail entities.
However, this method only considers the reference information
as the measurement criterion without taking into account the
potential impact of it on the reasoning process of the model. In
this paper, we propose a novel method that utilizes factual information
interactions. Firstly, we learn static representations of
entities based on their neighborhood information. Subsequently,
we learn relation adaptive representations by incorporating the
reference information. This interactive modeling strengthens the
association between entity representations and task relations
while suppressing irrelevant relations. Extensive experiments
demonstrate that our model outperforms state-of-the-art methods
on two public datasets. Remarkably, on the NELL-One dataset
for one-shot link prediction, our model achieves an improvement
of 11.8% in MRR compared to the best baseline model.

Indexed ByEI
Sub direction classification自然语言处理
planning direction of the national heavy laboratory小样本高噪声数据学习
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57229
Collection舆论大数据科学与技术应用联合实验室
Corresponding AuthorWang ZK(王子康)
Affiliation1.中国科学院自动化研究所
2.中国科学院大学人工智能学院
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li JL,Wang ZK,Li LJ,et al. Relation Adaptive Representation Learning Based on Factual Information Interaction for One-Shot Knowledge Graph Completion[C],2024.
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