SEEK: Segmented Embedding of Knowledge Graphs

• URL: http://arxiv.org/abs/2005.00856v3
• Date: Tue, 23 Jun 2020 03:27:31 GMT
• Title: SEEK: Segmented Embedding of Knowledge Graphs
• Authors: Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu
• Abstract summary: We propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations.
• Score: 77.5307592941209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at \url{https://github.com/Wentao-Xu/SEEK}.

Related papers

• Resilience in Knowledge Graph Embeddings [1.90894751866253]
  We give a unified definition of resilience, encompassing several factors such as generalisation, performance consistency, distribution adaption, and robustness. Our survey results show that most of the existing works focus on a specific aspect of resilience, namely robustness.
  arXiv  Detail & Related papers  (2024-10-28T16:04:22Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• Visual Commonsense based Heterogeneous Graph Contrastive Learning [79.22206720896664]
  We propose a heterogeneous graph contrastive learning method to better finish the visual reasoning task. Our method is designed as a plug-and-play way, so that it can be quickly and easily combined with a wide range of representative methods.
  arXiv  Detail & Related papers  (2023-11-11T12:01:18Z)
• DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph [59.583555454424]
  We propose a novel disenTangled representation learning framework for discrete-time Dynamic graphs, namely DyTed. We specially design a temporal-clips contrastive learning task together with a structure contrastive learning to effectively identify the time-invariant and time-varying representations respectively.
  arXiv  Detail & Related papers  (2022-10-19T14:34:12Z)
• VEM$^2$L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion [14.537509860565706]
  We propose a plug-and-play framework VEM2L over sparse Knowledge Graphs to fuse knowledge extracted from text and structure messages into a unity. Specifically, we partition knowledge acquired by models into two nonoverlapping parts. We also propose a new fusion strategy proved by Variational EM algorithm to fuse the generalization ability of models.
  arXiv  Detail & Related papers  (2022-07-04T15:50:21Z)
• A Graph-Enhanced Click Model for Web Search [67.27218481132185]
  We propose a novel graph-enhanced click model (GraphCM) for web search. We exploit both intra-session and inter-session information for the sparsity and cold-start problems.
  arXiv  Detail & Related papers  (2022-06-17T08:32:43Z)
• REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction [30.829001748700637]
  Relation extraction is a challenging task that aims to extract all hidden relational facts from the text. There is no unified framework that works well under various relation extraction settings. We propose a knowledge-enhanced generative model to mitigate these two issues. Our model achieves superior performance on multiple benchmarks and settings, including WebNLG, NYT10, and TACRED.
  arXiv  Detail & Related papers  (2022-06-10T13:59:38Z)
• Mutual Graph Learning for Camouflaged Object Detection [31.422775969808434]
  A major challenge is that intrinsic similarities between foreground objects and background surroundings make the features extracted by deep model indistinguishable. We design a novel Mutual Graph Learning model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain. In contrast to most mutual learning approaches that use a shared function to model all between-task interactions, MGL is equipped with typed functions for handling different complementary relations.
  arXiv  Detail & Related papers  (2021-04-03T10:14:39Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)
• Neural Stochastic Block Model & Scalable Community-Based Graph Learning [8.00785050036369]
  This paper proposes a scalable community-based neural framework for graph learning. The framework learns the graph topology through the task of community detection and link prediction. We look into two particular applications, the graph alignment and the anomalous correlation detection.
  arXiv  Detail & Related papers  (2020-05-16T03:28:50Z)

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.