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MGRAG: Semantic Subgraph Matching and Graph-Aware Caching for Multimodal Retrieval-Augmented Generation
Summary: MGRAG builds query-specific multimodal KGs lazily and retrieves evidence via semantics-aware path/subgraph matching rather than rigid topology. Its event-aware KV cache preserves graph-level structure while avoiding unnecessary token recomputation, improving RAG effectiveness and efficiency.
(summarized by gpt-5.6-luna on Aug 17 2026)
Paper ID
h90d6a1db7d5bba1e
Venue
VLDB
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,800 | 27.39%
DOI
10.14778/3819518.3819565
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
1.
Yubo Wang
(Hong Kong University of Science and Technology)
2.
Haoyang Li
(Hong Kong Polytechnic University)
3.
Lei Chen
(Hong Kong University of Science and Technology)
BibTeX Citation
Copy BibTeX
@article{wang_vldb26,
title = {{MGRAG: Semantic Subgraph Matching and Graph-Aware Caching for Multimodal Retrieval-Augmented Generation}},
author = {Wang, Yubo and Li, Haoyang and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {9},
pages = {2480--2493},
doi = {10.14778/3819518.3819565},
url = {https://doi.org/10.14778/3819518.3819565},
year = {2026}
}
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Pagerank
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6.8550917e-05
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A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction
2024
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