计算机科学与探索2026，Vol.20Issue(5)：1455-1464,10.DOI:10.3778/j.issn.1673-9418.2506054
多模态信息与门控注意力协同驱动的知识图谱补全方法
Knowledge Graph Completion Method Driven by Multimodal Information and Gated Attention Collaboration
摘要
Abstract
Knowledge graph completion aims to infer missing triples based on existing structural information,thereby enhancing the completeness and reasoning capabilities of the graph.Traditional structure-based embedding models perform well in modeling entity-relation interactions but often struggle with sparse entities and complex semantic relations due to limited representational capacity.With the recent advancements in pretrained language models,incorporating textual semantics of entities has emerged as a promising direction.However,effectively integrating structural and semantic infor-mation remains a challenge.To address the insufficient structural modeling and inadequate semantic fusion in existing methods,this paper proposes a hybrid model named HST-KG(hybrid struct text KG-model),which integrates structural information and textual semantics.Specifically,TuckER is employed as the structural encoder to capture entity-relation interaction patterns,while BERT(bidirectional encoder representations from transformers)is utilized to extract contextual semantics from entity and relation descriptions.Furthermore,a gated attention fusion mechanism is designed to dynamically weight structural and semantic embeddings,enabling adaptive multimodal collaborative modeling.Experimental results on two benchmark datasets,FB15K-237 and WN18RR,demonstrate that the proposed HST-KG outperforms the latest semantic-enhanced model ISA-KGC.On FB15K-237,HST-KG achieves improvements of 2.2 and 6.5 percentage points in Hits@1 and Hits@3,respectively;on WN18RR,it achieves gains of 0.7 percentage points in MRR and 0.8 percentage points in Hits@10.These results validate the effectiveness of the proposed method in complex relation modeling and sparse entity prediction.关键词
知识图谱补全/多模态融合/结构化嵌入/预训练语言模型/门控注意力机制Key words
knowledge graph completion/multimodal fusion/structured embedding/pretrained language model/gated attention mechanism分类
信息技术与安全科学引用本文复制引用
李亚峥,赵妍,刘宇航,季伟东,杨建柏..多模态信息与门控注意力协同驱动的知识图谱补全方法[J].计算机科学与探索,2026,20(5):1455-1464,10.基金项目
黑龙江省自然科学基金(PL2024F007).This work was supported by the Natural Science Foundation of Heilongjiang Province(PL2024F007). （PL2024F007）
