基于背景结构感知的小样本知识图谱补全

doi:10.11896/jsjkx.250100107

Abstract

Abstract: Few-shot knowledge graph completion aims to predict unseen facts in long-tail relationships within knowledge graphs using only a small number of reference data.The key challenge of this task lies in how to efficiently encode entity and relation features under conditions of data scarcity,and to construct an effective triplet scoring function.Existing few-shot knowledge graph completion models generally overlook the impact of entity pair contextual information on both entity encoding and the scoring function,while also suffering from insufficient relation representation learning.To address these issues,this paper proposes a background-structure-aware few-shot knowledge graph completion model—BSA.Firstly,it designs a metric for entity pair contextual interaction,which guides the model to focus attention on neighbor nodes that are structurally similar to the central entity by measuring the structural influence of neighboring entities,thereby reducing the negative impact of noisy neighbors.Secondly,during the relation representation learning phase,it incorporates background relation information from the knowledge graph that is semantically and structurally similar to the target relation to enhance its embedding representation.Finally,it introduces a contextual interaction metric for the head-tail entity pair in the triplet scoring function to improve the model’s reasoning capability for complex relations.Experimental results show that,compared to the best results from baseline models,the BSA model improves MRR,Hit@5,and Hit@1 by 0.4 percentage points,0.8 percentage points,and 0.5 percentage points percentage points on the NELL-One dataset,respectively,and improves MRR,Hit@10,and Hit@5 by 1.9 percentage points,2.2 percentage points,and 2.2 percentage points on the Wiki-One dataset,respectively,demonstrating the effectiveness and feasibility of the proposed method.

Key words: Few-shot knowledge graph completion, Background structure-aware, Representation learning, Attention mechanism

CLC Number:

TP391.1

ZHANG Jing, PAN Jinghao, JIANG Wenchao. Background Structure-aware Few-shot Knowledge Graph Completion[J].Computer Science, 2026, 53(2): 331-341.

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Background Structure-aware Few-shot Knowledge Graph Completion

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