TaxoBell: Gaussian Box Embeddings for Self-Supervised Taxonomy Expansion

• URL: http://arxiv.org/abs/2601.09633v1
• Date: Wed, 14 Jan 2026 17:08:37 GMT
• Title: TaxoBell: Gaussian Box Embeddings for Self-Supervised Taxonomy Expansion
• Authors: Sahil Mishra, Srinitish Srinivasan, Srikanta Bedathur, Tanmoy Chakraborty,
• Abstract summary: Taxonomies form the backbone of structured knowledge representation across diverse domains.<n>Existing automated methods rely on point-based vector embeddings.<n>Box embeddings offer a promising alternative by enabling containment and disjointness.
• Score: 25.5809347473818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Taxonomies form the backbone of structured knowledge representation across diverse domains, enabling applications such as e-commerce catalogs, semantic search, and biomedical discovery. Yet, manual taxonomy expansion is labor-intensive and cannot keep pace with the emergence of new concepts. Existing automated methods rely on point-based vector embeddings, which model symmetric similarity and thus struggle with the asymmetric "is-a" 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 boundaries, (ii) no notion of semantic uncertainty, and (iii) limited capacity to represent polysemy or ambiguity. We address these shortcomings with TaxoBell, a Gaussian box embedding framework that translates between box geometries and multivariate Gaussian distributions, where means encode semantic location and covariances encode uncertainty. Energy-based optimization yields stable optimization, robust modeling of ambiguous concepts, and interpretable hierarchical reasoning. Extensive experimentation on five benchmark datasets demonstrates that TaxoBell significantly outperforms eight state-of-the-art taxonomy expansion baselines by 19% in MRR and around 25% in Recall@k. We further demonstrate the advantages and pitfalls of TaxoBell with error analysis and ablation studies.

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