CASIA OpenIR  > 舆论大数据科学与技术应用联合实验室
BERT-FKGC: Text-Enhanced Few-Shot Representation Learning for Knowledge Graphs
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

In recent years, few-shot knowledge graph completion
(FKGC) emerged as a prominent research problem, focused
on utilizing a limited number of reference entity pairs to complete
triples with unseen relations. Recent studies have attempted
addressing this problem by modeling interactions between head
and tail entities. However, existing FKGC methods represent
semantics predominantly based on the neighborhood information
of entities in the knowledge graph, thus can only infer the hidden
and unobserved relations within the knowledge graph, limiting
their reasoning capabilities. To overcome these limitations, we
introduce text descriptions to FKGC and propose BERT-FKGC,
a model capable of learning the integrated distribution of both the
entity text descriptions and neighborhood information. By using
a gating network that allows the model to dynamically select
weights, our method can flexibly combine neighborhood information
and textual descriptions. Besides addressing the prediction
of unseen relations, our method is also capable of representing
unseen entities. To validate the effectiveness of our model, we
introduce a new dataset, FB15K-237-One, which includes textual
descriptions for entities. We conduct extensive experiments on
the FB15K-237-One dataset to validate the superiority of BERTFKGC.

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/173211/57228
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. BERT-FKGC: Text-Enhanced Few-Shot Representation Learning for Knowledge Graphs[C],2024.
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