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A Pre-training Framework for Knowledge Graph Completion

arXiv 2302.02614 ↗First posted 6 Feb 2023cs.AIcited by 0

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Abstract (arXiv)

Knowledge graph completion (KGC) is one of the effective methods to identify new facts in knowledge graph. Except for a few methods based on graph network, most of KGC methods trend to be trained based on independent triples, while are difficult to take a full account of the information of global network connection contained in knowledge network. To address these issues, in this study, we propose a simple and effective Network-based Pre-training framework for knowledge graph completion (termed NetPeace), which takes into account the information of global network connection and local triple relationships in knowledge graph. Experiments show that in NetPeace framework, multiple KGC models yields consistent and significant improvements on benchmarks (e.g., 36.45% Hits@1 and 27.40% MRR improvements for TuckER on FB15k-237), especially dense knowledge graph. On the challenging low-resource task, NetPeace that benefits from the global features of KG achieves higher performance (104.03% MRR and 143.89% Hit@1 improvements at most) than original models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 6 Feb 2023.

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21 of 36 references.

References · 21

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  4. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  5. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  6. 2016Complex Embeddings for Simple Link Predictioncited by 897
  7. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  8. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  9. 2015LINE: Large-scale Information Network Embeddingcited by 582
  10. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  11. 2017DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoningcited by 265
  12. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
9 more
  1. 2017Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learningcited by 161
  2. 2018Canonical Tensor Decomposition for Knowledge Base Completioncited by 150
  3. 2018Multi-Hop Knowledge Graph Reasoning with Reward Shapingcited by 102
  4. 2019Learning to Exploit Long-term Relational Dependencies in Knowledge Graphscited by 83
  5. 2019Interaction Embeddings for Prediction and Explanation in Knowledge Graphscited by 40
  6. 2018Hypernetwork Knowledge Graph Embeddingscited by 38
  7. 2018M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Searchcited by 34
  8. 2019Relation Adversarial Network for Low Resource Knowledge Graph Completioncited by 24
  9. 2016STransE: a novel embedding model of entities and relationships in knowledge basescited by 17

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