Relation Learning Completion Model for Few-shot Knowledge Graphs

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Relation Learning Completion Model for Few-shot Knowledge Graphs

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[1]LI Weijun,GU Jianlai,ZHANG Xinyong,et al.Relation Learning Completion Model for Few-shot Knowledge Graphs[J].Journal of Zhengzhou University (Engineering Science),2024,45(04):53-61.[doi:10.13705/ j.issn.1671-6833.2024.01.016]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 04 Page number: 53-61 Column: Public date: 2024-06-16

Title:: Relation Learning Completion Model for Few-shot Knowledge Graphs

Author(s):: LI Weijun; GU Jianlai; ZHANG Xinyong; GAO Yuxiao; LIU Jintong; School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China

Keywords:: knowledge graph completion; few-shot relation; neighborhood aggregation; link prediction

CLC:: TP391TP183

DOI:: 10.13705/ j.issn.1671-6833.2024.01.016

Abstract:: In few-show knowledge graphs, the representation of relationships between entity pairs was diverse and complex. However, existing few-show knowledge graph completion methods commonly suffered from insufficient re lational learning capabilities and the neglect of contextual semantics associated with entities. To address these chal lenges, a novel approach called the few-shot relation learning completion model (FRLC) was proposed. Firstly, during the process of aggregating high-order neighborhood entity information, a gating mechanism was introduced to mitigate the adverse effects of noise on neighbors while enriching the representation of central entities. Secondly, in the phase of relation representation learning, the correlations among entity pairs in a reference set were leveraged to obtain more accurate relationship representations. Lastly, within the Transformer-based learning framework, an LSTM structure was incorporated to further capture contextual semantic information of entities and relationships, which was used for predicting new factual knowledge. To validate the effectiveness of FRLC, comparative experi ments were conducted on the publicly available NELL-One and Wiki-One datasets, in which FRLC was compared with six few-shot knowledge graph completion models and five traditional models for 5-shot link prediction. The ex perimental results showed improvements in FRLC across four metrics: MRR, Hits@10, Hits@5, and Hits@1, demonstrating the model′s effectiveness.

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Last Update: 2024-06-14