mlsanityData to 14 Oct 2026

KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation

arXiv 2309.14770 ↗First posted 26 Sep 2023cs.CLcited by 6

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Abstract (arXiv)

Knowledge graph completion (KGC) revolves around populating missing triples in a knowledge graph using available information. Text-based methods, which depend on textual descriptions of triples, often encounter difficulties when these descriptions lack sufficient information for accurate prediction-an issue inherent to the datasets and not easily resolved through modeling alone. To address this and ensure data consistency, we first use large language models (LLMs) to generate coherent descriptions, bridging the semantic gap between queries and answers. Secondly, we utilize inverse relations to create a symmetric graph, thereby providing augmented training samples for KGC. Additionally, we employ the label information inherent in knowledge graphs (KGs) to enhance the existing contrastive framework, making it fully supervised. These efforts have led to significant performance improvements on the WN18RR and FB15k-237 datasets. According to standard evaluation metrics, our approach achieves a 4.2% improvement in Hit@1 on WN18RR and a 3.4% improvement in Hit@3 on FB15k-237, demonstrating superior performance.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 31 of its 70 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

31 of 70 references.

References · 31

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19 more
  1. 2019KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representationcited by 356
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  3. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  4. 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 175
  5. 2020A Survey on Knowledge Graph-Based Recommender Systemscited by 170
  6. 2020RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphscited by 118
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 2.

6 papers.

  1. 2024Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graphcited by 6
  2. 2025Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuningcited by 4
  3. 2024Deep Sparse Latent Feature Models for Knowledge Graph Completioncited by 0
  4. 2024Enhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approachcited by 0
  5. 2026When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarkingcited by 0
  6. 2026Reversing Arrows in Large Language Modelscited by 0