RelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion
📰 ArXiv cs.AI
Learn how RelBall uses quaternion rotation for knowledge graph completion to predict missing links in real-world graphs
Action Steps
- Implement RelBall using quaternion rotation to model symmetric, antisymmetric, inverse, and commutative composition patterns in knowledge graphs
- Compare the performance of RelBall with existing models like RotatE on benchmark datasets
- Apply RelBall to real-world knowledge graphs to predict missing links and evaluate its effectiveness
- Configure RelBall to handle semantic hierarchy and other complex relational patterns
- Test RelBall's ability to capture diverse relational patterns and improve graph coverage
Who Needs to Know This
Data scientists and AI engineers working on knowledge graph completion tasks can benefit from this research to improve their models' performance and handle diverse relational patterns
Key Insight
💡 RelBall uses quaternion rotation to effectively model diverse relational patterns in knowledge graphs, outperforming existing models like RotatE
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🤖 RelBall: a new approach to knowledge graph completion using quaternion rotation 📈 #KGCompletion #RelBall
Key Takeaways
Learn how RelBall uses quaternion rotation for knowledge graph completion to predict missing links in real-world graphs
Full Article
Title: RelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion
Abstract:
arXiv:2606.27967v1 Announce Type: new Abstract: Real-world knowledge graphs are often incomplete, lacking many valid facts. Knowledge Graph Completion (KGC) aims to predict missing links using known triples, thereby enhancing graph coverage. A key challenge is modeling diverse relational patterns such as symmetry, antisymmetry, inversion, composition and semantic hierarchy. Existing models such as RotatE can capture symmetric, antisymmetric, inverse, and commutative composition patterns, yet str
Abstract:
arXiv:2606.27967v1 Announce Type: new Abstract: Real-world knowledge graphs are often incomplete, lacking many valid facts. Knowledge Graph Completion (KGC) aims to predict missing links using known triples, thereby enhancing graph coverage. A key challenge is modeling diverse relational patterns such as symmetry, antisymmetry, inversion, composition and semantic hierarchy. Existing models such as RotatE can capture symmetric, antisymmetric, inverse, and commutative composition patterns, yet str
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