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Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction

Published: 15 February 2022 Publication History

Abstract

We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i.e., link prediction). Given a knowledge graph, NoGE constructs a single graph considering entities and relations as individual nodes. NoGE then computes weights for edges among nodes based on the co-occurrence of entities and relations. Next, NoGE proposes Dual Quaternion Graph Neural Networks (DualQGNN) and utilizes DualQGNN to update vector representations for entity and relation nodes. NoGE then adopts a score function to produce the triple scores. Comprehensive experimental results show that NoGE obtains state-of-the-art results on three new and difficult benchmark datasets CoDEx for knowledge graph completion.

Supplementary Material

MP4 File (WSDM22-de004.mp4)
We propose NoGE to integrate co-occurrence statistics among entities and relations into graph neural networks for knowledge graph completion

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  • (2025)Improving inference via rich path information and logic rules for document-level relation extractionKnowledge and Information Systems10.1007/s10115-024-02336-8Online publication date: 27-Jan-2025
  • (2024)Enhancing Heterogeneous Knowledge Graph Completion with a Novel GAT-based ApproachACM Transactions on Knowledge Discovery from Data10.1145/363947218:4(1-20)Online publication date: 13-Feb-2024
  • (2024)Knowledge graph relation prediction based on graph transformationApplied Intelligence10.1007/s10489-024-06080-y55:4Online publication date: 30-Dec-2024
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  1. Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction

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      cover image ACM Conferences
      WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
      February 2022
      1690 pages
      ISBN:9781450391320
      DOI:10.1145/3488560
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      Published: 15 February 2022

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      Author Tags

      1. graph neural networks
      2. knowledge graph completion
      3. knowledge graph embeddings
      4. quaternion

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      Cited By

      View all
      • (2025)Improving inference via rich path information and logic rules for document-level relation extractionKnowledge and Information Systems10.1007/s10115-024-02336-8Online publication date: 27-Jan-2025
      • (2024)Enhancing Heterogeneous Knowledge Graph Completion with a Novel GAT-based ApproachACM Transactions on Knowledge Discovery from Data10.1145/363947218:4(1-20)Online publication date: 13-Feb-2024
      • (2024)Knowledge graph relation prediction based on graph transformationApplied Intelligence10.1007/s10489-024-06080-y55:4Online publication date: 30-Dec-2024
      • (2024)Link Prediction in Knowledge Graph with Feature EnhancementProceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic10.1007/978-3-031-70235-8_47(524-536)Online publication date: 3-Sep-2024
      • (2023)Inferring Complementary and Substitutable Products Based on Knowledge Graph ReasoningMathematics10.3390/math1122470911:22(4709)Online publication date: 20-Nov-2023
      • (2023)Event Prediction using Case-Based Reasoning over Knowledge GraphsProceedings of the ACM Web Conference 202310.1145/3543507.3583201(2383-2391)Online publication date: 30-Apr-2023
      • (2023)Deep Outdated Fact Detection in Knowledge Graphs2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00184(1443-1452)Online publication date: 4-Dec-2023
      • (2023)Research on Supply Chain Knowledge Graph Inference Method Based on Quaternion Embedding2023 9th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA60676.2023.10429271(868-874)Online publication date: 15-Dec-2023
      • (2023)Community preserving adaptive graph convolutional networks for link prediction in attributed networksKnowledge-Based Systems10.1016/j.knosys.2023.110589272:COnline publication date: 19-Jul-2023
      • (2023)ASLEEP: A Shallow neural modEl for knowlEdge graph comPletionNeural Information Processing10.1007/978-981-99-1642-9_9(98-109)Online publication date: 14-Apr-2023

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