Sample and Feature Enhanced Few-Shot Knowledge Graph Completion

Kai Zhang, Daokun Zhang, Ning Liu, Yonghua Yang, Yonghui Xu, Zhongmin Yan, Hui Li, Lizhen Cui

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Knowledge graph completion is to infer missing/new entities or relations in knowledge graphs. The long-tail distribution of relations leads to the few-shot knowledge graph completion problem. Existing solutions do not thoroughly solve this problem, with the few training samples still deteriorating knowledge graph completion performance. In this paper, we propose a novel data augmentation mechanism to overcome the learning difficulty caused by few training samples, and a novel feature fusion scheme to reinforce data augmentation. Specifically, we use a conditional generative model to increase the number of entity samples on both entity structure and textual content views, and adaptively fuse entity structural and textual features to get informative entity representations. We then integrate adaptive feature fusion and generative sample augmentation with few-shot relation inference into an end-to-end learning framework. We conduct extensive experiments on five real-world knowledge graphs, showing the significant advantage of the proposed algorithm over state-of-the-art baselines, as well as the effectiveness of the proposed feature fusion and sample augmentation components.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 28th International Conference, DASFAA 2023, Proceedings
EditorsXin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao, Hongzhi Yin
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783031306716
Publication statusPublished - 2023
Externally publishedYes
Event28th International Conference on Database Systems for Advanced Applications, DASFAA 2023 - Tianjin, China
Duration: 17 Apr 202320 Apr 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13944 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on Database Systems for Advanced Applications, DASFAA 2023


  • Data Augmentation
  • Feature Fusion
  • Few-Shot Learning
  • Knowledge Graph Completion

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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