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20192026
most citedKGClean: An Embedding Powered Knowledge Graph Cleaning Framework

7 citations · 12 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.DB2026

PiLLar: Matching for Pivot Table Schema via LLM-guided Monte-Carlo Tree Search

Yunjun Gao, Chuangyu Ouyang, Congcong Ge +1

Pivot tables are ubiquitous in data lakes of modern data ecosystems, making accurate schema matching over pivot tables a key prerequisite for data integration. In this paper, we fo…

cs.DB20261 cited

DTBench: A Synthetic Benchmark for Document-to-Table Extraction

Yuxiang Guo, Zhuoran Du, Nan Tang +3

Document-to-table (Doc2Table) extraction derives structured tables from unstructured documents under a target schema, enabling reliable and verifiable SQL-based data analytics. Alt…

cs.DB2025

Accelerating High-Dimensional Nearest Neighbor Search with Dynamic Query Preference

Yifan Zhu, Ruijie Zhao, Zhonggen Li +4

Approximate Nearest Neighbor Search (ANNS) has emerged as an essential operation in modern database and AI systems. While graph-based methods like NSG demonstrate state-of-the-art…

cs.DB20211 cited

CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration

Congcong Ge, Pengfei Wang, Lu Chen +3

Entity Resolution (ER) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, a…

cs.DB20207 cited

KGClean: An Embedding Powered Knowledge Graph Cleaning Framework

Congcong Ge, Yunjun Gao, Honghui Weng +3

The quality assurance of the knowledge graph is a prerequisite for various knowledge-driven applications. We propose KGClean, a novel cleaning framework powered by knowledge graph…

cs.DB20193 cited

A Hybrid Data Cleaning Framework using Markov Logic Networks

Yunjun Gao, Congcong Ge, Xiaoye Miao +3

With the increase of dirty data, data cleaning turns into a crux of data analysis. Most of the existing algorithms rely on either qualitative techniques (e.g., data rules) or quant…