arXiv:2601.09633v2 Announce Type: replace Abstract: Taxonomies form the backbone of structured knowledge representation across diverse domains, enabling applications such as e-commerce and semantic search. Yet, manual taxonomy expansion is labor-intensive and slow. Existing methods rely on point-based vector embeddings, which model symmetric similarity and thus struggle with the asymmetric relationships that are fundamental to taxonomies. Box embeddings offer a promising alternative by enabling containment and disjointness, but they face key issues: (i) unstable gradients at the intersection b
The increasing complexity and scale of AI applications necessitate more sophisticated knowledge representation methods to move beyond simple keyword matching and towards contextual understanding.
Improved taxonomy expansion methods like TaxoBell can significantly enhance the accuracy and efficiency of AI systems in knowledge management, e-commerce, and semantic search.
This research introduces Gaussian Box Embeddings, offering a novel approach to modeling asymmetric relationships in taxonomies, which could lead to more robust and scalable knowledge representation systems.
- · AI/ML researchers
- · E-commerce platforms
- · Semantic search engines
- · Knowledge graph developers
- · manual taxonomy curators
- · companies relying on outdated knowledge representation
- · systems with limited contextual understanding
More accurate and automated content categorization and product recommendations.
Reduced operational costs for businesses managing large knowledge bases and improved user experiences in complex systems.
Enhanced overall intelligence of agentic AI systems that rely on structured knowledge for decision-making and task execution.
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Read at arXiv cs.CL