GAHE: Geometry-Aware Embedding for Hyper-Relational Knowledge Graph Representation
文献类型：期刊论文
| 作者 | Cao, Zongsheng1; Xu, Qianqian2; Yang, Zhiyong3; He, Yuan4; Cao, Xiaochun5; Huang, Qingming3 |
| 刊名 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
 |
| 出版日期 | 2025-07-01 |
| 卷号 | 21期号:7页码:26 |
| 关键词 | Knowledge Graph Hyper-relational Learning Multicurvature Spaces Geometry Aware |
| ISSN号 | 1551-6857 |
| DOI | 10.1145/3733602 |
| 英文摘要 | Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capabilities. Each fact in an HKG consists of a main triple supplemented by attribute-value qualifiers that provide additional contextual information. Due to the complexity of hyper-relations, HKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, often mixed together. However, previous work mainly embeds HKGs into Euclidean space, limiting their ability to capture these complex geometric structures simultaneously. To address this challenge, we propose a novel model called geometryaware hyper-relational embedding (GAHE). Specifically, GAHE adopts a multi-curvature geometry-aware approach by modeling HKGs in Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature) in a unified framework. In this way, it can integrate spaceinvariant and space-specific features to accurately capture the diverse structures in HKGs. In addition, GAHE introduces a module termed hyper-relational subspace learning, which allocates multiple sub-relations for each hyper-relation. It enables the exploitation of abundant latent semantic interactions and facilitates the exploration of fine-grained semantics between attribute-value pairs and hyper-relations across multiple subspaces. Furthermore, we provide theoretical guarantees that GAHE is fully expressive and capable of modeling a wide range of semantic patterns for hyper-relations. Empirical evaluations demonstrate that GAHE achieves state-of-the-art results on both hyper-relational and binary-relational benchmarks. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001540889700002 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42000]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Cao, Zongsheng |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Informat Secur SKLOIS, Inst Informat Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China 4.Alibaba Grp, Secur Dept, Beijing, Peoples R China 5.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen Campus, Shenzhen, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Zongsheng,Xu, Qianqian,Yang, Zhiyong,et al. GAHE: Geometry-Aware Embedding for Hyper-Relational Knowledge Graph Representation[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2025,21(7):26. |
| APA | Cao, Zongsheng,Xu, Qianqian,Yang, Zhiyong,He, Yuan,Cao, Xiaochun,&Huang, Qingming.(2025).GAHE: Geometry-Aware Embedding for Hyper-Relational Knowledge Graph Representation.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,21(7),26. |
| MLA | Cao, Zongsheng,et al."GAHE: Geometry-Aware Embedding for Hyper-Relational Knowledge Graph Representation".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 21.7(2025):26. |
入库方式： OAI收割
来源：计算技术研究所
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