SGKGE: Semantically Guided Knowledge Graph Embeddings via Complementary Latent Representations

doi:10.1109/TNNLS.2025.3545773

	SGKGE: Semantically Guided Knowledge Graph Embeddings via Complementary Latent Representations
	Liu, Ruizhou 1; Cao, Zongsheng 2; Wu, Zhe 3; Wu, Yiling 3; Xu, Qianqian 4; Huang, Qingming 5
	2025-04-24
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
页码	14
摘要	Knowledge graph (KG) completion is a challenging yet essential task that has attracted increasing attention in recent years. While entities in KGs typically present complex semantics (a phenomenon known as polysemy), previous works primarily focus on holistic but often inaccurate representations of entities, neglecting the diversity of their semantics. This limitation results in suboptimal representations for entities within KGs. To address this issue, we propose a new method termed semantically guided KG embeddings (SGKGE), which captures the precise semantics of entities in KGs from a semantics-guided perspective. Specifically, SGKGE first guides the learning of holistic semantics of entities through a hyperbolic manifold with learnable shared curvature and a geometric attention-fusion module, facilitating efficient reasoning. Subsequently, SGKGE captures fine-grained semantics through a set of Cartesian product Riemannian manifolds with distinct curvatures, coupled with a semantic interactions module. This approach enables SGKGE to produce more accurate entity semantics and enhance downstream applications. Experimental results demonstrate that our model achieves state-of-the-art performance on six well-established KG completion benchmarks. The release code is available at https://github.com/RuizhouLiu/SGKGE.
关键词	Semantics Manifolds Vectors Training Tensors Knowledge graphs Animals Translation Tail Matrix decomposition Hyperbolic manifold knowledge graph embedding (KGE)
DOI	10.1109/TNNLS.2025.3545773
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[62441232] ; National Natural Science Foundation of China[62472238] ; National Natural Science Foundation of China[U21B2038]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001481872600001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40653
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Qianqian; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, Beijing 100085, Peoples R China 3.Pengcheng Lab, Shenzhen 518055, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Ruizhou,Cao, Zongsheng,Wu, Zhe,et al. SGKGE: Semantically Guided Knowledge Graph Embeddings via Complementary Latent Representations[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2025:14.
APA	Liu, Ruizhou,Cao, Zongsheng,Wu, Zhe,Wu, Yiling,Xu, Qianqian,&Huang, Qingming.(2025).SGKGE: Semantically Guided Knowledge Graph Embeddings via Complementary Latent Representations.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14.
MLA	Liu, Ruizhou,et al."SGKGE: Semantically Guided Knowledge Graph Embeddings via Complementary Latent Representations".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2025):14.