A Semantics-aware Approach for Graph Edit Distance Estimation over Knowledge Graphs
Summary: KG-native GED estimation: SEABED is a semantics-aware GNN that models local semantic dependencies and global consistency between two knowledge graphs, unlike prior simple-graph GED predictors. Improves MAE up to 66.7% and accuracy up to 70.5% on real KGs, with no extra runtime. (summarized by gpt-5.4-mini on Apr 12 2026)
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Authors
- 1. Yingli Zhou (Chinese University of Hong Kong)
- 2. Huizhong Wang (Chinese University of Hong Kong)
- 3. Chenhao Ma (Chinese University of Hong Kong)
- 4. Yixiang Fang (Chinese University of Hong Kong)
BibTeX Citation
@article{zhou_vldb26,
title = {{A Semantics-aware Approach for Graph Edit Distance Estimation over Knowledge Graphs}},
author = {Zhou, Yingli and Wang, Huizhong and Ma, Chenhao and Fang, Yixiang},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {6},
pages = {1226--1239},
doi = {10.14778/3797919.3797930},
url = {https://doi.org/10.14778/3797919.3797930},
year = {2026}
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