Geometry Interaction Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2206.12418v1
• Date: Fri, 24 Jun 2022 08:33:43 GMT
• Title: Geometry Interaction Knowledge Graph Embeddings
• Authors: Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang
• Abstract summary: We propose Geometry Interaction knowledge graph Embeddings (GIE), which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. Our proposed GIE can capture a richer set of relational information, model key inference patterns, and enable expressive semantic matching across entities.
• Score: 153.69745042757066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a single geometric space such as Euclidean space (zero curved), hyperbolic space (negatively curved) or hyperspherical space (positively curved) to maintain their specific geometric structures (e.g., chain, hierarchy and ring structures). However, the topological structure of KGs appears to be complicated, since it may contain multiple types of geometric structures simultaneously. Therefore, embedding KGs in a single space, no matter the Euclidean space, hyperbolic space or hyperspheric space, cannot capture the complex structures of KGs accurately. To overcome this challenge, we propose Geometry Interaction knowledge graph Embeddings (GIE), which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. Theoretically, our proposed GIE can capture a richer set of relational information, model key inference patterns, and enable expressive semantic matching across entities. Experimental results on three well-established knowledge graph completion benchmarks show that our GIE achieves the state-of-the-art performance with fewer parameters.

Related papers

• IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion [97.58125811599383]
  Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge. We propose a novel Multi-curvature shared and specific Embedding (IME) model for TKGC tasks. IME incorporates two key properties, namely space-shared property and space-specific property.
  arXiv  Detail & Related papers  (2024-03-28T23:31:25Z)
• Improving embedding of graphs with missing data by soft manifolds [51.425411400683565]
  The reliability of graph embeddings depends on how much the geometry of the continuous space matches the graph structure. We introduce a new class of manifold, named soft manifold, that can solve this situation. Using soft manifold for graph embedding, we can provide continuous spaces to pursue any task in data analysis over complex datasets.
  arXiv  Detail & Related papers  (2023-11-29T12:48:33Z)
• Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning [69.6810940330906]
  We propose a novel contrastive learning framework to learn high-quality graph embedding. Specifically, we design the alignment metric that effectively captures the hierarchical data-invariant information. We show that in the hyperbolic space one has to address the leaf- and height-level uniformity which are related to properties of trees.
  arXiv  Detail & Related papers  (2023-10-27T15:31:42Z)
• Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform [29.205221688430733]
  The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. Recent explorations of the complex hyperbolic geometry further improved the hyperbolic embeddings for capturing a variety of hierarchical structures. This paper aims to utilize the representation capacity of the complex hyperbolic geometry in multi-relational KG embeddings.
  arXiv  Detail & Related papers  (2022-11-07T15:46:00Z)
• Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs [32.47146018135465]
  Two-view knowledge graphs (KGs) jointly represent two components: an ontology view for abstract and commonsense concepts, and an instance view for specific entities. Most recent works on embedding KGs assume that the entire KG belongs to only one of the two views but not both simultaneously. We construct a dual-geometric space embedding model (DGS) that models two-view KGs using a complex non-Euclidean geometric space.
  arXiv  Detail & Related papers  (2022-09-19T05:11:10Z)
• Geometry Contrastive Learning on Heterogeneous Graphs [50.58523799455101]
  This paper proposes a novel self-supervised learning method, termed as Geometry Contrastive Learning (GCL) GCL views a heterogeneous graph from Euclidean and hyperbolic perspective simultaneously, aiming to make a strong merger of the ability of modeling rich semantics and complex structures. Extensive experiments on four benchmarks data sets show that the proposed approach outperforms the strong baselines.
  arXiv  Detail & Related papers  (2022-06-25T03:54:53Z)
• ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion [78.8942067357231]
  ExpressivE embeds pairs of entities as points and relations as hyper-parallelograms in the virtual triple space. We show that ExpressivE is competitive with state-of-the-art KGEs and even significantly outperforms them on W18RR.
  arXiv  Detail & Related papers  (2022-06-08T23:34:39Z)
• Ultrahyperbolic Knowledge Graph Embeddings [41.85886045379838]
  We present an ultrahyperbolic KG embedding in an ultrahyperbolic (or pseudo-Riemannian) manifold. In particular, we model each relation as a pseudo-orthogonal transformation that preserves the pseudo-Riemannian bilinear form. Experimental results on three standard KGs show that UltraE outperforms previous Euclidean- and hyperbolic-based approaches.
  arXiv  Detail & Related papers  (2022-06-01T12:31:26Z)
• Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations [14.279419014064047]
  Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks. Many KGEs use the flat geometry which renders them incapable of preserving complex structures. We propose a neuro differential KGE that embeds nodes of a KG on the trajectories of Ordinary Differential Equations (ODEs)
  arXiv  Detail & Related papers  (2020-10-13T20:53:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.