Matrix factorization (MF) is a widely used backbone for modeling large relational data due to its simplicity, scalability, and interpretability. However, classical MF uses a single shared latent basis, which can be overly restrictive for heterogeneous matrices. In this paper, we propose Masked Mixture Factorization (MM...
Yong-chan Park, Jeongyoung Lee, Seungjoo Lee et al.· Proceedings of the 32nd ACM...· 0 citations
TiRano (Tensorized Relation-aware temporal reasoning for knowledge graph completion), an accurate and efficient tensor-based temporal reasoning framework for TKGC, is proposed, which consistently outperforms state-of-the-art TKGC methods in terms of both prediction accuracy and efficiency.
Seungjoo Lee, Yong-chan Park, U. Kang· Proceedings of the 32nd ACM...· 0 citations
Given a partially observed Temporal Knowledge Graph (TKG), how can we accurately predict missing entities? Unlike static knowledge graphs, TKGs encode facts within temporal contexts, requiring models to reason over both graph structure and time. However, existing TKGC approaches often sample neighbors solely based on t...
Seungjoo Lee, Yong-chan Park, U. Kang· Proceedings of the 32nd ACM...· 0 citations
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