mlsanityData to 7 Oct 2026

Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction

arXiv 2209.01397 ↗First posted 3 Sep 2022cs.LGcited by 7

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Abstract (arXiv)

Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs). Existing studies for DEKGs only focus on predicting enclosing links, i.e., predicting links inside the emerging KG. The bridging links, which carry the evolutionary information from the original KG to DEKG, have not been investigated by previous work so far. To fill in the gap, we propose a novel model entitled DEKG-ILP (Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction) that consists of the following two components. (1) The module CLRM (Contrastive Learning-based Relation-specific Feature Modeling) is developed to extract global relation-based semantic features that are shared between original KGs and DEKGs with a novel sampling strategy. (2) The module GSM (GNN-based Subgraph Modeling) is proposed to extract the local subgraph topological information around each link in KGs. The extensive experiments conducted on several benchmark datasets demonstrate that DEKG-ILP has obvious performance improvements compared with state-of-the-art methods for both enclosing and bridging link prediction. The source code is available online.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 29 of its 54 references that are in the map. First posted on arXiv 3 Sep 2022.

Builds on

29 of 54 references.

References · 29

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17 more
  1. 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 173
  2. 2017A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkcited by 143
  3. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  4. 2019Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Predictioncited by 133
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 2.

7 papers.

  1. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  2. 2023GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropycited by 9
  3. 2023Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphscited by 2
  4. 2025Norm Augmented Graph AutoEncoders for Link Predictioncited by 1
  5. 2024One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completioncited by 0
  6. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0
  7. 2026A Survey on GNN-based Link Prediction: Techniques, Applications, and Challengescited by 0