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20242026
most citedCardinality Estimation on Hyper-relational Knowledge Graphs

1 citations · 1 across the 8 of their papers we have counts for

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cs.LG2026

One Score, Two Decisions: Selective Prediction on the Rare-Disease Tail

Zhaoyang Jiang, Zhizhong Fu, Yunsoo Kim +5

Given a patient's clinical findings, a diagnostic system ranks possible diseases and must decide when to endorse its first prediction or defer it for review. This decision is usual…

cs.LG2026

Presentation, Not Mechanism: A Render Confound in Deprecation-Aware Memory Evaluation

Zhaoyang Jiang, Zhizhong Fu, Zicheng Li +5

AI systems increasingly retrieve from records that revise themselves: issue threads, encyclopedic histories, policy logs, and long conversations. The challenge is not only finding…

cs.LG2025

AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs

Yubo Wang, Haoyang Li, Fei Teng +1

Graph-based retrieval-augmented generation (Graph-based RAG) has demonstrated significant potential in enhancing Large Language Models (LLMs) with structured knowledge. However, ex…

cs.LG2025

Understanding the Embedding Models on Hyper-relational Knowledge Graph

Yubo Wang, Shimin Di, Zhili Wang +4

Recently, Hyper-relational Knowledge Graphs (HKGs) have been proposed as an extension of traditional Knowledge Graphs (KGs) to better represent real-world facts with additional qua…

cs.LG2024★ 1 cited

Cardinality Estimation on Hyper-relational Knowledge Graphs

Fei Teng, Haoyang Li, Shimin Di +1

Cardinality Estimation (CE) for query is to estimate the number of results without execution, which is an effective index in query optimization. Recently, CE for queries over knowl…