A novel method enhances knowledge graph completion by using LLMs with topological information through in-context learning, working in both transductive and inductive settings.
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https://arxiv.org/abs/2412.08742
🤔 Original Problem:
Knowledge graphs often suffer from incompleteness, limiting their real-world impact. Traditional completion methods struggle with complex relationships and new entities.
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🔧 Solution in this Paper:
→ The method generates ontologies from knowledge graphs using LLMs to extract structured knowledge and node categories
→ It incorporates two types of topological information: ontology topology and graph topology
→ For each relation, it creates node sets containing head and tail nodes
→ The system prompts LLMs to predict node categories while maintaining consistency
→ During prediction, it uses ontology to infer missing node categories
→ The approach leverages graph topology to identify candidate solutions
→ Chain-of-thought reasoning is applied for final predictions
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🎯 Key Insights:
→ LLMs can effectively generate structured ontologies from raw graph data
→ Topological information significantly improves prediction accuracy
→ The method requires no additional training, making it immediately applicable
→ Performance depends on graph density and connection richness
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