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Zhongtian Hu

2 accepted papers

2025

SaCa: A Highly Compatible Reinforcing Framework for Knowledge Graph Embedding via Structural Pattern Contrast

EMNLP 2025

Knowledge Graph Embedding (KGE) seeks to learn latent representations of entities and relations to support knowledge-driven AI systems. However, existing KGE approaches often exhibit a growing discrepancy between the learned embedding space and the intrinsic structural semantics of the underlying kn

Cited by 0SourcePDFScholar
2024

Improving Knowledge Graph Completion with Structure-Aware Supervised Contrastive Learning

EMNLP 2024main

Knowledge Graphs (KGs) often suffer from incomplete knowledge, which which restricts their utility. Recently, Contrastive Learning (CL) has been introduced to Knowledge Graph Completion (KGC), significantly improving the discriminative capabilities of KGC models and setting new benchmarks in perform…

Cited by 1SourcePDFScholar