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 struggle with non-commutative composition. Rotate3D addresses this by introducing non-commutativity via
The continuous improvement of Knowledge Graph Completion (KGC) models is crucial as real-world knowledge graphs remain largely incomplete, hindering AI system performance and expanding capabilities.
Advanced KGC models like RelBall enhance the ability of AI systems to understand complex relationships in data, leading to more robust and accurate AI applications in various domains.
The explicit modeling of diverse and complex relational patterns, including non-commutative composition, improves the foundational capabilities of knowledge representation within AI.
- · AI researchers
- · Data science industry
- · Companies with large knowledge bases
- · AI models relying on incomplete data
- · Systems with simplistic relational modeling
Improved KGC leads to more accurate and reliable AI systems across various applications.
Enhanced knowledge graphs could accelerate drug discovery, material science, and other knowledge-intensive fields.
More sophisticated AI reasoning could make autonomous agents more capable, accelerating their adoption in complex tasks.
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Read at arXiv cs.AI