QuanTaxo: A Quantum Approach to Self-Supervised Taxonomy Expansion

Abstract: A taxonomy is a hierarchical graph containing knowledge to provide valuable insights for various web applications. Online retail organizations like Microsoft and Amazon utilize taxonomies to improve product recommendations and optimize advertisement by enhancing query interpretation. However, the manual construction of taxonomies requires significant human effort. As web content continues to expand at an unprecedented pace, existing taxonomies risk becoming outdated, struggling to incorporate new and emerging information effectively. As a consequence, there is a growing need for dynamic taxonomy expansion to keep them relevant and up-to-date. Existing taxonomy expansion methods often rely on classical word embeddings to represent entities. However, these embeddings fall short in capturing hierarchical polysemy, where an entity's meaning can vary based on its position in the hierarchy and its surrounding context. To address this challenge, we introduce QuanTaxo, an innovative quantum-inspired framework for taxonomy expansion. QuanTaxo encodes entity representations in quantum space, effectively modeling hierarchical polysemy by leveraging the principles of Hilbert space to capture interference effects between entities, yielding richer and more nuanced representations. Comprehensive experiments on four real-world benchmark datasets show that QuanTaxo significantly outperforms classical embedding models, achieving substantial improvements of 18.45% in accuracy, 20.5% in Mean Reciprocal Rank, and 17.87% in Wu & Palmer metrics across eight classical embedding-based baselines. We further highlight the superiority of QuanTaxo through extensive ablation and case studies.