IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion

• URL: http://arxiv.org/abs/2403.19881v1
• Date: Thu, 28 Mar 2024 23:31:25 GMT
• Title: IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
• Authors: Jiapu Wang, Zheng Cui, Boyue Wang, Shirui Pan, Junbin Gao, Baocai Yin, Wen Gao,
• Abstract summary: 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.
• Score: 97.58125811599383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and reflecting the dynamic nature of the real world. Typically, TKGs contain complex geometric structures, with various geometric structures interwoven. However, existing Temporal Knowledge Graph Completion (TKGC) methods either model TKGs in a single space or neglect the heterogeneity of different curvature spaces, thus constraining their capacity to capture these intricate geometric structures. In this paper, we propose a novel Integrating Multi-curvature shared and specific Embedding (IME) model for TKGC tasks. Concretely, IME models TKGs into multi-curvature spaces, including hyperspherical, hyperbolic, and Euclidean spaces. Subsequently, IME incorporates two key properties, namely space-shared property and space-specific property. The space-shared property facilitates the learning of commonalities across different curvature spaces and alleviates the spatial gap caused by the heterogeneous nature of multi-curvature spaces, while the space-specific property captures characteristic features. Meanwhile, IME proposes an Adjustable Multi-curvature Pooling (AMP) approach to effectively retain important information. Furthermore, IME innovatively designs similarity, difference, and structure loss functions to attain the stated objective. Experimental results clearly demonstrate the superior performance of IME over existing state-of-the-art TKGC models.

Related papers

• Unraveling the Localized Latents: Learning Stratified Manifold Structures in LLM Embedding Space with Sparse Mixture-of-Experts [3.9426000822656224]
  We conjecture that in large language models, the embeddings live in a local manifold structure with different dimensions depending on the perplexities and domains of the input data. By incorporating an attention-based soft-gating network, we verify that our model learns specialized sub-manifolds for an ensemble of input data sources.
  arXiv  Detail & Related papers  (2025-02-19T09:33:16Z)
• MIND: Microstructure INverse Design with Generative Hybrid Neural Representation [25.55691106041371]
  inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. We present a novel generative model that integrates latent diffusion with Holoplane, an advanced hybrid neural representation that simultaneously encodes both geometric and physical properties. Our approach generalizes across multiple microstructure classes, enabling the generation of diverse, tileable microstructures with significantly improved property accuracy and enhanced control over geometric validity.
  arXiv  Detail & Related papers  (2025-02-01T20:25:47Z)
• Gaussian Entanglement Measure: Applications to Multipartite Entanglement of Graph States and Bosonic Field Theory [50.24983453990065]
  An entanglement measure based on the Fubini-Study metric has been recently introduced by Cocchiarella and co-workers. We present the Gaussian Entanglement Measure (GEM), a generalization of geometric entanglement measure for multimode Gaussian states. By providing a computable multipartite entanglement measure for systems with a large number of degrees of freedom, we show that our definition can be used to obtain insights into a free bosonic field theory.
  arXiv  Detail & Related papers  (2024-01-31T15:50:50Z)
• A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation [89.86345494602642]
  Existing methods are limited in weak-temporal modeling capability. We propose a Decoupled Scoupled Framework (DeST) to address the issues. DeST significantly outperforms current state-of-the-art methods with less computational complexity.
  arXiv  Detail & Related papers  (2023-12-10T09:11:39Z)
• 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 Interaction Knowledge Graph Embeddings [153.69745042757066]
  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.
  arXiv  Detail & Related papers  (2022-06-24T08:33:43Z)
• Joint Characterization of the Cryospheric Spectral Feature Space [0.0]
  characterization of feature space dimensionality, geometry and topology can provide guidance for effective model design. This study compares and contrast two approaches for identifying feature space basis vectors via dimensionality reduction. Joint characterization reveals distinct continua for snow-firn gradients on different parts of the Greenland Ice Sheet. The ability of PC+t-SNE joint characterization to produce a physically interpretable spectral feature spaces suggests that this characterization might be extended to the much higher dimensional hyperspectral feature space of all terrestrial land cover.
  arXiv  Detail & Related papers  (2021-12-02T17:04:50Z)
• The role of feature space in atomistic learning [62.997667081978825]
  Physically-inspired descriptors play a key role in the application of machine-learning techniques to atomistic simulations. We introduce a framework to compare different sets of descriptors, and different ways of transforming them by means of metrics and kernels. We compare representations built in terms of n-body correlations of the atom density, quantitatively assessing the information loss associated with the use of low-order features.
  arXiv  Detail & Related papers  (2020-09-06T14:12:09Z)
• Geodesics in fibered latent spaces: A geometric approach to learning correspondences between conditions [62.997667081978825]
  This work introduces a geometric framework and a novel network architecture for creating correspondences between samples of different conditions. Under this formalism, the latent space is a fiber bundle stratified into a base space encoding conditions, and a fiber space encoding the variations within conditions.
  arXiv  Detail & Related papers  (2020-05-16T03:14:52Z)

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.